diff --git a/.github/workflows/did_sim.yml b/.github/workflows/did_sim.yml
index 75c3ffca..199220d3 100644
--- a/.github/workflows/did_sim.yml
+++ b/.github/workflows/did_sim.yml
@@ -19,6 +19,7 @@ jobs:
         script: [
           'scripts/did/did_pa_atte_coverage.py',
           'scripts/did/did_cs_atte_coverage.py',
+          'scripts/did/did_pa_multi.py',
         ]
 
     steps:
@@ -48,20 +49,27 @@ jobs:
       with:
         ref: ${{ env.TARGET_BRANCH }}
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        version: "0.6.11"
+
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.12'
+        python-version-file: "monte-cover/pyproject.toml"
 
-    - name: Install dependencies
+    - name: Install Monte-Cover
       run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements.txt
+        cd monte-cover
+        uv venv
+        uv sync
 
     - name: Install DoubleML from correct branch
       run: |
-        pip uninstall -y doubleml
-        pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
+        source monte-cover/.venv/bin/activate
+        uv pip uninstall doubleml
+        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
@@ -69,7 +77,9 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: python ${{ matrix.script }}
+      run: |
+        source monte-cover/.venv/bin/activate
+        uv run ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |
diff --git a/.github/workflows/plr_sim.yml b/.github/workflows/plr_sim.yml
index 7d95d3c4..973a5b4d 100644
--- a/.github/workflows/plr_sim.yml
+++ b/.github/workflows/plr_sim.yml
@@ -50,20 +50,27 @@ jobs:
       with:
         ref: ${{ env.TARGET_BRANCH }}
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        version: "0.6.11"
+  
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.12'
+        python-version-file: "monte-cover/pyproject.toml"
 
-    - name: Install dependencies
+    - name: Install Monte-Cover
       run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements.txt
+        cd monte-cover
+        uv venv
+        uv sync
 
     - name: Install DoubleML from correct branch
       run: |
-        pip uninstall -y doubleml
-        pip install git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}
+        source monte-cover/.venv/bin/activate
+        uv pip uninstall doubleml
+        uv pip install "doubleml @ git+https://github.com/DoubleML/doubleml-for-py@${{ env.DML_BRANCH }}"
 
     - name: Set up Git configuration
       run: |
@@ -71,7 +78,9 @@ jobs:
         git config --global user.email 'github-actions@github.com'
 
     - name: Run scripts
-      run: python ${{ matrix.script }}
+      run: |
+        source monte-cover/.venv/bin/activate
+        uv run ${{ matrix.script }}
 
     - name: Commit any existing changes
       run: |
diff --git a/.gitignore b/.gitignore
index c18dd8d8..628f882c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,5 @@
 __pycache__/
+
+# Logs
+monte-cover/logs/
+*.log
\ No newline at end of file
diff --git a/doc/_website.yml b/doc/_website.yml
index 6dd3260f..4beb1519 100644
--- a/doc/_website.yml
+++ b/doc/_website.yml
@@ -25,6 +25,7 @@ website:
           - plm/pliv.qmd
       - text: "DID"
         menu:
+          - did/did_multi.qmd
           - did/did_pa.qmd
           - did/did_cs.qmd
       - text: "SSM"
diff --git a/doc/did/did_multi.qmd b/doc/did/did_multi.qmd
new file mode 100644
index 00000000..0cfed2e0
--- /dev/null
+++ b/doc/did/did_multi.qmd
@@ -0,0 +1,312 @@
+---
+title: "DiD for Panel Data over Multiple Periods"
+
+jupyter: python3
+---
+
+```{python}
+#| echo: false
+
+import numpy as np
+import pandas as pd
+from itables import init_notebook_mode, show, options
+
+init_notebook_mode(all_interactive=True)
+
+def highlight_range(s, level=0.95, dist=0.05, props=''):
+    color_grid = np.where((s >= level-dist) &
+                          (s <= level+dist), props, '')
+    return color_grid
+
+
+def color_coverage(df, level):
+    # color coverage column order is important
+    styled_df = df.apply(
+        highlight_range,
+        level=level,
+        dist=1.0,
+        props='color:black;background-color:red',
+        subset=["Coverage", "Uniform Coverage"])
+    styled_df = styled_df.apply(
+        highlight_range,
+        level=level,
+        dist=0.1,
+        props='color:black;background-color:yellow',
+        subset=["Coverage", "Uniform Coverage"])
+    styled_df = styled_df.apply(
+        highlight_range,
+        level=level,
+        dist=0.05,
+        props='color:white;background-color:darkgreen',
+        subset=["Coverage", "Uniform Coverage"])
+
+    # set all coverage values to bold
+    styled_df = styled_df.set_properties(
+        **{'font-weight': 'bold'},
+        subset=["Coverage", "Uniform Coverage"])
+    return styled_df
+
+
+def make_pretty(df, level, n_rep):
+    styled_df = df.style.hide(axis="index")
+    # Format only float columns
+    float_cols = df.select_dtypes(include=['float']).columns
+    styled_df = styled_df.format({col: "{:.3f}" for col in float_cols})
+
+    # color coverage column order is important
+    styled_df = color_coverage(styled_df, level)
+    caption = f"Coverage for {level*100}%-Confidence Interval over {n_rep} Repetitions"
+
+    return show(styled_df, caption=caption)
+```
+
+## ATTE Coverage
+
+The simulations are based on the  the [make_did_CS2021](https://docs.doubleml.org/dev/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs:
+
+ - Type 1: Linear outcome model and treatment assignment
+ - Type 4: Nonlinear outcome model and treatment assignment
+ - Type 6: Randomized treatment assignment and nonlinear outcome model
+
+The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). 
+
+::: {.callout-note title="Metadata"  collapse="true"}
+
+```{python}
+#| echo: false
+metadata_file = '../../results/did/did_multi_metadata.csv'
+metadata_df = pd.read_csv(metadata_file)
+print(metadata_df.T.to_string(header=False))
+```
+
+:::
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_detailed.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+
+### Experimental Score
+
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+## Aggregated Effects
+
+These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did).
+
+The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). 
+
+### Group Effects
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_group.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+### Time Effects
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_time.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+### Event Study Aggregation
+
+```{python}
+#| echo: false
+
+# set up data
+df = pd.read_csv("../../results/did/did_multi_eventstudy.csv", index_col=None)
+
+assert df["repetition"].nunique() == 1
+n_rep = df["repetition"].unique()[0]
+
+display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"]
+```
+
+#### Observational Score 
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "observational"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
+
+#### Experimental Score
+
+The results are only valid for the DGP 6, as the experimental score assumes a randomized treatment assignment.
+
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.95
+
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_95, level, n_rep)
+```
+
+```{python}
+#| echo: false
+score = "experimental"
+level = 0.9
+
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
+make_pretty(df_ate_9, level, n_rep)
+```
\ No newline at end of file
diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd
index 61d482cd..0a0dbcce 100644
--- a/doc/plm/plr.qmd
+++ b/doc/plm/plr.qmd
@@ -90,11 +90,11 @@ display_columns = ["Learner g", "Learner m", "Bias", "CI Length", "Coverage"]
 ### Partialling out
 
 ```{python}
-#| echo: false
+# | echo: false
 score = "partialling out"
 level = 0.95
 
-df_ate_95 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_95 = df[(df["level"] == level) & (df["Score"] == score)][display_columns]
 df_ate_95.rename(columns={"Learner g": "Learner l"}, inplace=True)
 make_pretty(df_ate_95, level, n_rep)
 ```
@@ -104,7 +104,7 @@ make_pretty(df_ate_95, level, n_rep)
 score = "partialling out"
 level = 0.9
 
-df_ate_9 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 df_ate_9.rename(columns={"Learner g": "Learner l"}, inplace=True)
 make_pretty(df_ate_9, level, n_rep)
 ```
@@ -118,7 +118,7 @@ For the IV-type score, the learners `ml_l` and `ml_g` are both set to the same t
 score = "IV-type"
 level = 0.95
 
-df_ate_95 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_95 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 make_pretty(df_ate_95, level, n_rep)
 ```
 
@@ -127,7 +127,7 @@ make_pretty(df_ate_95, level, n_rep)
 score = "IV-type"
 level = 0.9
 
-df_ate_9 = df[(df['level'] == level) & (df["score"] == score)][display_columns]
+df_ate_9 = df[(df['level'] == level) & (df["Score"] == score)][display_columns]
 make_pretty(df_ate_9, level, n_rep)
 ```
 
diff --git a/monte-cover/.python-version b/monte-cover/.python-version
new file mode 100644
index 00000000..e4fba218
--- /dev/null
+++ b/monte-cover/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/monte-cover/README.md b/monte-cover/README.md
new file mode 100644
index 00000000..e69de29b
diff --git a/monte-cover/pyproject.toml b/monte-cover/pyproject.toml
new file mode 100644
index 00000000..357e36c3
--- /dev/null
+++ b/monte-cover/pyproject.toml
@@ -0,0 +1,49 @@
+[project]
+name = "montecover"
+version = "0.1.0"
+description = "A Monte Carlo coverage simulation package"
+readme = "README.md"
+authors = [
+    { name = "SvenKlaassen", email = "sven.klaassen@uni-hamburg.de" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "black>=25.1.0",
+    "doubleml[rdd]>=0.9.3",
+    "ipykernel>=6.29.5",
+    "itables>=2.2.5",
+    "joblib>=1.4.2",
+    "lightgbm>=4.6.0",
+    "numpy>=2.2.4",
+    "pandas>=2.2.3",
+    "pyyaml>=6.0.2",
+    "ruff>=0.11.0",
+    "scikit-learn>=1.5.2",
+]
+
+[project.scripts]
+monte-cover = "montecover:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.black]
+line-length = 127
+target-version = ['py311', 'py312']
+preview = true
+
+[tool.ruff]
+# max line length for black
+line-length = 127
+target-version = "py312"
+
+
+[tool.ruff.lint]
+# all rules can be found here: https://beta.ruff.rs/docs/rules/
+select = ["E", "F", "W", "I"]
+ignore = [
+    # Use `is` and `is not` for type comparisons, or `isinstance()` for
+    # isinstance checks
+    "E721",
+]
diff --git a/monte-cover/src/montecover/__init__.py b/monte-cover/src/montecover/__init__.py
new file mode 100644
index 00000000..8d60b6b8
--- /dev/null
+++ b/monte-cover/src/montecover/__init__.py
@@ -0,0 +1,13 @@
+"""Monte Carlo coverage simulation package for statistical methods."""
+
+from importlib.metadata import version
+
+__version__ = version("montecover")
+
+from .base import BaseSimulation
+
+__all__ = ["BaseSimulation"]
+
+
+def main() -> None:
+    print("Hello from monte-cover! Run your simulations here.")
diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py
new file mode 100644
index 00000000..957f0feb
--- /dev/null
+++ b/monte-cover/src/montecover/base.py
@@ -0,0 +1,341 @@
+import logging
+import os
+import sys
+import time
+import warnings
+from abc import ABC, abstractmethod
+from datetime import datetime
+from itertools import product
+from typing import Any, Dict, Optional
+
+import doubleml as dml
+import numpy as np
+import pandas as pd
+import yaml
+from joblib import Parallel, delayed, parallel_backend
+
+
+class BaseSimulation(ABC):
+    """Abstract base class for simulation studies."""
+
+    def __init__(
+        self,
+        config_file: str,
+        suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
+    ):
+        # Load config first to get default values if needed
+        self.config_file = config_file
+        self.config = self._load_config(config_file)
+
+        # Apply parameters from config
+        self.simulation_parameters = self.config.get("simulation_parameters", {})
+        self.repetitions = self.simulation_parameters.get("repetitions", 20)
+        self.max_runtime = self.simulation_parameters.get("max_runtime", 5.5 * 3600)
+        self.random_seed = self.simulation_parameters.get("random_seed", 42)
+        self.default_n_jobs = self.simulation_parameters.get("n_jobs", 1)
+        self.suppress_warnings = suppress_warnings
+
+        # Set up logging
+        self._setup_logging(log_level, log_file)
+        self.logger.info(f"Loaded configuration from {config_file}")
+
+        # Results storage
+        self.results = dict()
+        self.result_summary = None
+
+        # Timing
+        self.start_time = None
+        self.end_time = None
+        self.total_runtime = None
+
+        # Set up environment
+        if suppress_warnings:
+            warnings.simplefilter(action="ignore", category=UserWarning)
+
+        np.random.seed(self.random_seed)
+        self.logger.info(f"Initialized simulation with random seed {self.random_seed}")
+
+        # Let child classes process any specific parameters
+        self.dgp_parameters = self.config.get("dgp_parameters", {})
+        self.dml_parameters = self.config.get("dml_parameters", {})
+        self.confidence_parameters = self.config.get("confidence_parameters", {})
+        self._process_config_parameters()
+
+    @abstractmethod
+    def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData:
+        """Generate data for the DoubleML simulation."""
+        pass
+
+    @abstractmethod
+    def run_single_rep(self, params: Dict[str, Any]) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        pass
+
+    @abstractmethod
+    def summarize_results(self) -> Dict[str, Any]:
+        """Summarize the simulation results."""
+        pass
+
+    def run_simulation(self, n_jobs=None):
+        """
+        Run the full simulation.
+
+        Parameters:
+        -----------
+        n_jobs : int, optional (default=None)
+            Number of jobs to run in parallel. If None, uses the value from the configuration file.
+            If 1, runs sequentially. If > 1, runs in parallel with the specified number of workers.
+            If -1, uses all available CPU cores .
+            If -2, uses all available CPU cores except one.
+        """
+        # Use n_jobs from parameter, or fall back to config value
+        n_jobs = n_jobs if n_jobs is not None else self.default_n_jobs
+        self._log_parameters(n_jobs=n_jobs)
+
+        if n_jobs == 1:
+            for i_rep in range(self.repetitions):
+                rep_start_time = time.time()
+                self.logger.info(f"Starting repetition {i_rep + 1}/{self.repetitions}")
+
+                if self._stop_simulation():
+                    break
+
+                # Running actual simulation
+                self._process_repetition(i_rep)
+
+                rep_end_time = time.time()
+                rep_duration = rep_end_time - rep_start_time
+                self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s")
+
+        else:
+            self.logger.info(f"Starting parallel execution with n_jobs={n_jobs}")
+            with parallel_backend("loky", inner_max_num_threads=1):
+                results = Parallel(n_jobs=n_jobs, verbose=10)(
+                    delayed(self._process_repetition)(i_rep)
+                    for i_rep in range(self.repetitions)
+                    if not self._stop_simulation()
+                )
+
+            # Process results from parallel execution
+            for worker_results in results:
+                if worker_results:  # Check if we have results
+                    for result_name, result_list in worker_results.items():
+                        if result_name not in self.results:
+                            self.results[result_name] = []
+                        self.results[result_name].extend(result_list)
+
+        self._process_results()
+
+    def save_results(self, output_path: str = "results", file_prefix: str = ""):
+        """Save the simulation results."""
+        os.makedirs(output_path, exist_ok=True)
+
+        metadata = pd.DataFrame(
+            {
+                "DoubleML Version": [dml.__version__],
+                "Script": [self.__class__.__name__],
+                "Date": [datetime.now().strftime("%Y-%m-%d %H:%M")],
+                "Total Runtime (minutes)": [self.total_runtime / 60],
+                "Python Version": [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"],
+                "Config File": [self.config_file],
+            }
+        )
+
+        for df_name, df in self.result_summary.items():
+            output_file = os.path.join(output_path, f"{file_prefix}_{df_name}.csv")
+            df.to_csv(output_file, index=False)
+            self.logger.info(f"Results saved to {output_file}")
+
+        metadata_file = os.path.join(output_path, f"{file_prefix}_metadata.csv")
+        metadata.to_csv(metadata_file, index=False)
+        self.logger.info(f"Metadata saved to {metadata_file}")
+
+    def save_config(self, output_path: str):
+        """Save the current configuration to a YAML file."""
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+        if not (output_path.endswith(".yaml") or output_path.endswith(".yml")):
+            output_path += ".yaml"
+            self.logger.warning(f"Adding .yaml extension to output path: {output_path}")
+
+        with open(output_path, "w") as file:
+            yaml.dump(self.config, file)
+
+        self.logger.info(f"Configuration saved to {output_path}")
+
+    def _load_config(self, config_path: str) -> Dict[str, Any]:
+        """Load configuration from a YAML file."""
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f"Config file not found: {config_path}")
+
+        if config_path.endswith(".yaml") or config_path.endswith(".yml"):
+            with open(config_path, "r") as file:
+                config = yaml.safe_load(file)
+        else:
+            raise ValueError(f"Unsupported config file format: {config_path}. Use .yaml or .yml")
+
+        return config
+
+    def _process_config_parameters(self):
+        """
+        Process any special configuration parameters.
+        Child classes should override this method to handle specific parameters.
+        """
+        pass
+
+    def _setup_logging(self, log_level: str, log_file: Optional[str]):
+        """Set up logging configuration."""
+        level = getattr(logging, log_level.upper())
+        self.logger = logging.getLogger(self.__class__.__name__)
+        self.logger.setLevel(level)
+
+        # Clear any existing handlers
+        if self.logger.hasHandlers():
+            self.logger.handlers.clear()
+
+        # Console handler
+        ch = logging.StreamHandler()
+        ch.setLevel(level)
+        formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+        ch.setFormatter(formatter)
+        self.logger.addHandler(ch)
+
+        # File handler if specified
+        if log_file:
+            os.makedirs(os.path.dirname(log_file), exist_ok=True)
+            fh = logging.FileHandler(log_file)
+            fh.setLevel(level)
+            fh.setFormatter(formatter)
+            self.logger.addHandler(fh)
+
+    def _log_parameters(self, n_jobs):
+        """Initialize timing and calculate parameter combination metrics."""
+        self.start_time = time.time()
+        self.logger.info("Starting simulation")
+        self.logger.info(f"DGP Parameters: {self.dgp_parameters}")
+        self.logger.info(f"DML Parameters: {self.dml_parameters}")
+        self.logger.info(f"Confidence Parameters: {self.confidence_parameters}")
+
+        # Calculate expected iterations
+        dgp_combinations = [len(v) for v in self.dgp_parameters.values()]
+        dml_combinations = [len(v) for v in self.dml_parameters.values()]
+        self.total_combinations = np.prod(dgp_combinations + dml_combinations)
+        self.total_iterations = self.total_combinations * self.repetitions
+
+        self.logger.info(f"Total parameter combinations: {self.total_combinations}")
+        self.logger.info(f"Expected total iterations: {self.total_iterations}")
+        if n_jobs <= 1:
+            self.logger.info("Running simulation sequentially")
+        else:
+            self.logger.info(f"Running simulation in parallel with {n_jobs} workers")
+
+    def _stop_simulation(self) -> bool:
+        """Check if simulation should be stopped based on criteria like runtime."""
+        # Check if maximum runtime is exceeded
+        if self.max_runtime and time.time() - self.start_time > self.max_runtime:
+            self.logger.warning("Maximum runtime exceeded. Stopping the simulation.")
+            return True
+        return False
+
+    def _process_repetition(self, i_rep):
+        """Process a single repetition with all parameter combinations."""
+        if self.suppress_warnings:
+            warnings.simplefilter(action="ignore", category=UserWarning)
+
+        i_param_comb = 0
+        rep_results = {}
+
+        # loop through all parameter combinations
+        for dgp_param_values in product(*self.dgp_parameters.values()):
+            dgp_params = dict(zip(self.dgp_parameters.keys(), dgp_param_values))
+            dml_data = self._generate_dml_data(dgp_params)
+
+            for dml_param_values in product(*self.dml_parameters.values()):
+                dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values))
+                i_param_comb += 1
+
+                comb_results = self._process_parameter_combination(i_rep, i_param_comb, dgp_params, dml_params, dml_data)
+
+                # Merge results
+                for result_name, result_list in comb_results.items():
+                    if result_name not in rep_results:
+                        rep_results[result_name] = []
+                    rep_results[result_name].extend(result_list)
+
+        return rep_results
+
+    def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_params, dml_data):
+        """Process a single parameter combination."""
+        # Log parameter combination
+        self.logger.debug(
+            f"Rep {i_rep+1}, Combo {i_param_comb}/{self.total_combinations}: " f"DGPs {dgp_params}, DML {dml_params}"
+        )
+        param_start_time = time.time()
+
+        try:
+            repetition_results = self.run_single_rep(dml_data, dml_params)
+
+            # Log timing
+            param_duration = time.time() - param_start_time
+            self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s")
+
+            # Process results
+            if repetition_results is None:
+                return {}
+
+            # Add metadata to results
+            processed_results = {}
+            for result_name, repetition_result in repetition_results.items():
+                processed_results[result_name] = []
+                for res in repetition_result:
+                    res["repetition"] = i_rep
+                    res.update(dgp_params)
+                    processed_results[result_name].append(res)
+
+            return processed_results
+
+        except Exception as e:
+            self.logger.error(
+                f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, " f"DML parameters {dml_params}: {str(e)}"
+            )
+            self.logger.exception("Exception details:")
+            return {}
+
+    def _process_results(self):
+        """Process collected results and log completion metrics."""
+        # Convert results to dataframes incrementally
+        for key, value in self.results.items():
+            self.results[key] = pd.DataFrame(value)
+
+        self.end_time = time.time()
+        self.total_runtime = self.end_time - self.start_time
+        self.logger.info(f"Simulation completed in {self.total_runtime:.2f}s")
+
+        # Summarize results
+        self.logger.info("Summarizing results")
+        self.result_summary = self.summarize_results()
+
+    @staticmethod
+    def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None):
+        """Compute coverage, CI length, and bias."""
+        lower_bound = confint.iloc[:, 0]
+        upper_bound = confint.iloc[:, 1]
+        coverage_mask = (lower_bound < oracle_thetas) & (oracle_thetas < upper_bound)
+
+        result_dict = {
+            "Coverage": np.mean(coverage_mask),
+            "CI Length": np.mean(upper_bound - lower_bound),
+            "Bias": np.mean(np.abs(thetas - oracle_thetas)),
+        }
+
+        if joint_confint is not None:
+            joint_lower_bound = joint_confint.iloc[:, 0]
+            joint_upper_bound = joint_confint.iloc[:, 1]
+            joint_coverage_mark = (joint_lower_bound < oracle_thetas) & (oracle_thetas < joint_upper_bound)
+
+            result_dict["Uniform Coverage"] = np.all(joint_coverage_mark)
+            result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound)
+
+        return result_dict
diff --git a/monte-cover/src/montecover/did/__init__.py b/monte-cover/src/montecover/did/__init__.py
new file mode 100644
index 00000000..5aecac30
--- /dev/null
+++ b/monte-cover/src/montecover/did/__init__.py
@@ -0,0 +1,5 @@
+"""Monte Carlo coverage simulations for DiD."""
+
+from montecover.did.did_pa_multi import DIDMultiCoverageSimulation
+
+__all__ = ["DIDMultiCoverageSimulation"]
diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py
new file mode 100644
index 00000000..2c4e601e
--- /dev/null
+++ b/monte-cover/src/montecover/did/did_pa_multi.py
@@ -0,0 +1,185 @@
+from typing import Any, Dict, Optional
+
+import doubleml as dml
+import numpy as np
+import pandas as pd
+from doubleml.did.datasets import make_did_CS2021
+from lightgbm import LGBMClassifier, LGBMRegressor
+from sklearn.linear_model import LinearRegression, LogisticRegression
+
+from montecover.base import BaseSimulation
+
+
+class DIDMultiCoverageSimulation(BaseSimulation):
+    """Simulation study for coverage properties of DoubleMLDIDMulti."""
+
+    def __init__(
+        self,
+        config_file: str,
+        suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
+    ):
+        super().__init__(
+            config_file=config_file,
+            suppress_warnings=suppress_warnings,
+            log_level=log_level,
+            log_file=log_file,
+        )
+
+        # Additional results storage for aggregated results
+        self.results_aggregated = []
+
+        # Calculate oracle values
+        self._calculate_oracle_values()
+
+    def _process_config_parameters(self):
+        """Process simulation-specific parameters from config"""
+        # Process ML models in parameter grid
+
+        assert "learners" in self.dml_parameters, "No learners specified in the config file"
+        for learner in self.dml_parameters["learners"]:
+            assert "ml_g" in learner, "No ml_g specified in the config file"
+            assert "ml_m" in learner, "No ml_m specified in the config file"
+
+            # Convert ml_g strings to actual objects
+            if learner["ml_g"][0] == "Linear":
+                learner["ml_g"] = ("Linear", LinearRegression())
+            elif learner["ml_g"][0] == "LGBM":
+                learner["ml_g"] = ("LGBM", LGBMRegressor(n_estimators=500, learning_rate=0.02, verbose=-1, n_jobs=1))
+            else:
+                raise ValueError(f"Unknown learner type: {learner['ml_g']}")
+
+            # Convert ml_m strings to actual objects
+            if learner["ml_m"][0] == "Linear":
+                learner["ml_m"] = ("Linear", LogisticRegression())
+            elif learner["ml_m"][0] == "LGBM":
+                learner["ml_m"] = ("LGBM", LGBMClassifier(n_estimators=500, learning_rate=0.02, verbose=-1, n_jobs=1))
+            else:
+                raise ValueError(f"Unknown learner type: {learner['ml_m']}")
+
+    def _calculate_oracle_values(self):
+        """Calculate oracle values for the simulation."""
+        self.logger.info("Calculating oracle values")
+
+        self.oracle_values = dict()
+        # Oracle values
+        df_oracle = make_did_CS2021(n_obs=int(1e6), dgp_type=1)  # does not depend on the DGP type
+        df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"]
+        self.oracle_values["detailed"] = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index()
+
+        # Oracle group aggregation
+        df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]]
+        self.oracle_values["group"] = df_oracle_post_treatment.groupby("d")["ite"].mean()
+
+        # Oracle time aggregation
+        self.oracle_values["time"] = df_oracle_post_treatment.groupby("t")["ite"].mean()
+
+        # Oracle eventstudy aggregation
+        df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - pd.to_datetime(
+            df_oracle["d"]
+        ).values.astype("datetime64[M]")
+        self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:]
+
+    def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        # Extract parameters
+        learner_g_name, ml_g = dml_params["learners"]["ml_g"]
+        learner_m_name, ml_m = dml_params["learners"]["ml_m"]
+        score = dml_params["score"]
+        in_sample_normalization = dml_params["in_sample_normalization"]
+
+        # Model
+        dml_model = dml.did.DoubleMLDIDMulti(
+            obj_dml_data=dml_data,
+            ml_g=ml_g,
+            ml_m=None if score == "experimental" else ml_m,
+            gt_combinations="standard",
+            score=score,
+            in_sample_normalization=in_sample_normalization,
+        )
+        dml_model.fit()
+        dml_model.bootstrap(n_rep_boot=2000)
+
+        # Oracle values for this model
+        oracle_thetas = np.full_like(dml_model.coef, np.nan)
+        for i, (g, _, t) in enumerate(dml_model.gt_combinations):
+            group_index = self.oracle_values["detailed"]["d"] == g
+            time_index = self.oracle_values["detailed"]["t"] == t
+            oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0]
+
+        result = {
+            "detailed": [],
+            "group": [],
+            "time": [],
+            "eventstudy": [],
+        }
+        for level in self.confidence_parameters["level"]:
+            level_result = dict()
+            level_result["detailed"] = self._compute_coverage(
+                thetas=dml_model.coef,
+                oracle_thetas=oracle_thetas,
+                confint=dml_model.confint(level=level),
+                joint_confint=dml_model.confint(level=level, joint=True),
+            )
+
+            for aggregation_method in ["group", "time", "eventstudy"]:
+                agg_obj = dml_model.aggregate(aggregation=aggregation_method)
+                agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000)
+
+                level_result[aggregation_method] = self._compute_coverage(
+                    thetas=agg_obj.aggregated_frameworks.thetas,
+                    oracle_thetas=self.oracle_values[aggregation_method].values,
+                    confint=agg_obj.aggregated_frameworks.confint(level=level),
+                    joint_confint=agg_obj.aggregated_frameworks.confint(level=level, joint=True),
+                )
+
+            # add parameters to the result
+            for res in level_result.values():
+                res.update(
+                    {
+                        "Learner g": learner_g_name,
+                        "Learner m": learner_m_name,
+                        "Score": score,
+                        "In-sample-norm.": in_sample_normalization,
+                        "level": level,
+                    }
+                )
+            for key, res in level_result.items():
+                result[key].append(res)
+
+        return result
+
+    def summarize_results(self):
+        """Summarize the simulation results."""
+        self.logger.info("Summarizing simulation results")
+
+        groupby_cols = ["Learner g", "Learner m", "Score", "In-sample-norm.", "DGP", "level"]
+        aggregation_dict = {
+            "Coverage": "mean",
+            "CI Length": "mean",
+            "Bias": "mean",
+            "Uniform Coverage": "mean",
+            "Uniform CI Length": "mean",
+            "repetition": "count",
+        }
+
+        result_summary = dict()
+        for result_name, result_df in self.results.items():
+            result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index()
+            self.logger.debug(f"Summarized {result_name} results")
+
+        return result_summary
+
+    def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData:
+        """Generate data for the simulation."""
+        data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"])
+        dml_data = dml.data.DoubleMLPanelData(
+            data,
+            y_col="y",
+            d_cols="d",
+            id_col="id",
+            t_col="t",
+            x_cols=["Z1", "Z2", "Z3", "Z4"],
+        )
+        return dml_data
diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py
new file mode 100644
index 00000000..f861d2bb
--- /dev/null
+++ b/monte-cover/src/montecover/plm/__init__.py
@@ -0,0 +1,5 @@
+"""Monte Carlo coverage simulations for PLM."""
+
+from montecover.plm.plr_ate import PLRATECoverageSimulation
+
+__all__ = ["PLRATECoverageSimulation"]
diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py
new file mode 100644
index 00000000..ac7f1c3b
--- /dev/null
+++ b/monte-cover/src/montecover/plm/plr_ate.py
@@ -0,0 +1,138 @@
+from typing import Any, Dict, Optional
+
+import doubleml as dml
+from doubleml.datasets import make_plr_CCDDHNR2018
+from lightgbm import LGBMRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LassoCV
+
+from montecover.base import BaseSimulation
+
+
+class PLRATECoverageSimulation(BaseSimulation):
+    """Simulation study for coverage properties of DoubleMLPLR for ATE estimation."""
+
+    def __init__(
+        self,
+        config_file: str,
+        suppress_warnings: bool = True,
+        log_level: str = "INFO",
+        log_file: Optional[str] = None,
+    ):
+        super().__init__(
+            config_file=config_file,
+            suppress_warnings=suppress_warnings,
+            log_level=log_level,
+            log_file=log_file,
+        )
+
+        # Additional results storage for aggregated results
+        self.results_aggregated = []
+
+        # Calculate oracle values
+        self._calculate_oracle_values()
+
+    def _process_config_parameters(self):
+        """Process simulation-specific parameters from config"""
+        # Process ML models in parameter grid
+        assert "learners" in self.dml_parameters, "No learners specified in the config file"
+        for learner in self.dml_parameters["learners"]:
+            assert "ml_g" in learner, "No ml_g specified in the config file"
+            assert "ml_m" in learner, "No ml_m specified in the config file"
+
+            # Convert ml_g strings to actual objects
+            learner["ml_g"] = self._convert_ml_string_to_object(learner["ml_g"][0])
+            learner["ml_m"] = self._convert_ml_string_to_object(learner["ml_m"][0])
+
+    def _convert_ml_string_to_object(self, ml_string):
+        """Convert a string to a machine learning object."""
+        if ml_string == "Lasso":
+            learner = LassoCV()
+        elif ml_string == "Random Forest":
+            learner = RandomForestRegressor(n_estimators=200, max_features=10, max_depth=5, min_samples_leaf=20)
+        elif ml_string == "LGBM":
+            learner = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1, n_jobs=1)
+        else:
+            raise ValueError(f"Unknown learner type: {ml_string}")
+
+        return (ml_string, learner)
+
+    def _calculate_oracle_values(self):
+        """Calculate oracle values for the simulation."""
+        self.logger.info("Calculating oracle values")
+
+        self.oracle_values = dict()
+        self.oracle_values["theta"] = self.dgp_parameters["theta"]
+
+    def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
+        """Run a single repetition with the given parameters."""
+        # Extract parameters
+        learner_g_name, ml_g = dml_params["learners"]["ml_g"]
+        learner_m_name, ml_m = dml_params["learners"]["ml_m"]
+        score = dml_params["score"]
+
+        # Model
+        dml_model = dml.DoubleMLPLR(
+            obj_dml_data=dml_data,
+            ml_l=ml_g,
+            ml_m=ml_m,
+            ml_g=ml_g if score == "IV-type" else None,
+            score=score,
+        )
+        dml_model.fit()
+
+        result = {
+            "coverage": [],
+        }
+        for level in self.confidence_parameters["level"]:
+            level_result = dict()
+            level_result["coverage"] = self._compute_coverage(
+                thetas=dml_model.coef,
+                oracle_thetas=self.oracle_values["theta"],
+                confint=dml_model.confint(level=level),
+                joint_confint=None,
+            )
+
+            # add parameters to the result
+            for res in level_result.values():
+                res.update(
+                    {
+                        "Learner g": learner_g_name,
+                        "Learner m": learner_m_name,
+                        "Score": score,
+                        "level": level,
+                    }
+                )
+            for key, res in level_result.items():
+                result[key].append(res)
+
+        return result
+
+    def summarize_results(self):
+        """Summarize the simulation results."""
+        self.logger.info("Summarizing simulation results")
+
+        # Group by parameter combinations
+        groupby_cols = ["Learner g", "Learner m", "Score", "level"]
+        aggregation_dict = {
+            "Coverage": "mean",
+            "CI Length": "mean",
+            "Bias": "mean",
+            "repetition": "count",
+        }
+
+        # Aggregate results (possibly multiple result dfs)
+        result_summary = dict()
+        for result_name, result_df in self.results.items():
+            result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index()
+            self.logger.debug(f"Summarized {result_name} results")
+
+        return result_summary
+
+    def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData:
+        """Generate data for the simulation."""
+        data = make_plr_CCDDHNR2018(
+            alpha=dgp_params["theta"], n_obs=dgp_params["n_obs"], dim_x=dgp_params["dim_x"], return_type="DataFrame"
+        )
+        dml_data = dml.DoubleMLData(data, "y", "d")
+        return dml_data
diff --git a/monte-cover/uv.lock b/monte-cover/uv.lock
new file mode 100644
index 00000000..57e1dd3c
--- /dev/null
+++ b/monte-cover/uv.lock
@@ -0,0 +1,1140 @@
+version = 1
+revision = 1
+requires-python = ">=3.12"
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
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index d6308e55..286a49ba 100644
--- a/results/did/did_cs_atte_coverage_metadata.csv
+++ b/results/did/did_cs_atte_coverage_metadata.csv
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diff --git a/results/did/did_multi_detailed.csv b/results/did/did_multi_detailed.csv
new file mode 100644
index 00000000..4b6d8700
--- /dev/null
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new file mode 100644
index 00000000..001f0d32
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diff --git a/results/did/did_multi_group.csv b/results/did/did_multi_group.csv
new file mode 100644
index 00000000..4577e7d6
--- /dev/null
+++ b/results/did/did_multi_group.csv
@@ -0,0 +1,49 @@
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diff --git a/results/did/did_multi_metadata.csv b/results/did/did_multi_metadata.csv
new file mode 100644
index 00000000..7bdadd5c
--- /dev/null
+++ b/results/did/did_multi_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
+0.10.dev0,DIDMultiCoverageSimulation,2025-04-28 13:09,156.835364429156,3.12.9,scripts/did/did_pa_multi_config.yml
diff --git a/results/did/did_multi_time.csv b/results/did/did_multi_time.csv
new file mode 100644
index 00000000..56028273
--- /dev/null
+++ b/results/did/did_multi_time.csv
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+Linear,Linear,observational,True,6,0.9,0.9013333333333333,1.0055268502646775,0.2433058652713604,0.9,1.1573364189497195,1000
+Linear,Linear,observational,True,6,0.95,0.953,1.198159142986716,0.2433058652713604,0.955,1.3448712678433115,1000
diff --git a/results/did/did_pa_atte_coverage.csv b/results/did/did_pa_atte_coverage.csv
index 890866ef..9119e34e 100644
--- a/results/did/did_pa_atte_coverage.csv
+++ b/results/did/did_pa_atte_coverage.csv
@@ -1,16 +1,16 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.051,2.162461753688773,2.169737683875206,1000
-LGBM,LGBM,experimental,False,1,0.95,0.094,2.576732109002647,2.169737683875206,1000
-LGBM,LGBM,experimental,False,2,0.9,0.377,2.1295044728560635,1.3213439323325022,1000
-LGBM,LGBM,experimental,False,2,0.95,0.475,2.5374610867049356,1.3213439323325022,1000
-LGBM,LGBM,experimental,False,3,0.9,0.47,1.8796286302073664,1.0014962629759643,1000
-LGBM,LGBM,experimental,False,3,0.95,0.584,2.239715655638386,1.0014962629759643,1000
-LGBM,LGBM,experimental,False,4,0.9,0.078,1.883011293050954,1.9173640236877347,1000
-LGBM,LGBM,experimental,False,4,0.95,0.122,2.243746347024319,1.9173640236877347,1000
-LGBM,LGBM,experimental,False,5,0.9,0.889,2.0642730548921726,0.5209994874770714,1000
-LGBM,LGBM,experimental,False,5,0.95,0.951,2.4597330580373264,0.5209994874770714,1000
-LGBM,LGBM,experimental,False,6,0.9,0.908,1.808638762699564,0.43376612813148785,1000
-LGBM,LGBM,experimental,False,6,0.95,0.948,2.155126011123672,0.43376612813148785,1000
+LGBM,LGBM,experimental,False,1,0.9,0.051,2.1624651397785515,2.1697505905302976,1000
+LGBM,LGBM,experimental,False,1,0.95,0.094,2.576736143777478,2.1697505905302976,1000
+LGBM,LGBM,experimental,False,2,0.9,0.378,2.1294819852681166,1.3213007503499328,1000
+LGBM,LGBM,experimental,False,2,0.95,0.475,2.537434291091179,1.3213007503499328,1000
+LGBM,LGBM,experimental,False,3,0.9,0.47,1.8796188347464011,1.0014962844281858,1000
+LGBM,LGBM,experimental,False,3,0.95,0.583,2.239703983626731,1.0014962844281858,1000
+LGBM,LGBM,experimental,False,4,0.9,0.078,1.8830006248174618,1.9173419242712182,1000
+LGBM,LGBM,experimental,False,4,0.95,0.122,2.243733635040054,1.9173419242712182,1000
+LGBM,LGBM,experimental,False,5,0.9,0.889,2.0642395778601936,0.5210006870170777,1000
+LGBM,LGBM,experimental,False,5,0.95,0.951,2.459693167693339,0.5210006870170777,1000
+LGBM,LGBM,experimental,False,6,0.9,0.908,1.8086231900602612,0.4337431389507495,1000
+LGBM,LGBM,experimental,False,6,0.95,0.948,2.1551074551794365,0.4337431389507495,1000
 LGBM,LGBM,experimental,True,1,0.9,0.049,2.159654648685151,2.17137681706751,1000
 LGBM,LGBM,experimental,True,1,0.95,0.099,2.573387237083486,2.17137681706751,1000
 LGBM,LGBM,experimental,True,2,0.9,0.373,2.129643042411216,1.319771507009933,1000
@@ -23,18 +23,18 @@ LGBM,LGBM,experimental,True,5,0.9,0.891,2.063666189492902,0.5200351889316859,100
 LGBM,LGBM,experimental,True,5,0.95,0.947,2.459009933312703,0.5200351889316859,1000
 LGBM,LGBM,experimental,True,6,0.9,0.897,1.8093537325759845,0.43669034939483387,1000
 LGBM,LGBM,experimental,True,6,0.95,0.944,2.1559779502779253,0.43669034939483387,1000
-LGBM,LGBM,observational,False,1,0.9,0.893,12.590352220156928,3.3786016989727083,1000
-LGBM,LGBM,observational,False,1,0.95,0.953,15.002329994503222,3.3786016989727083,1000
-LGBM,LGBM,observational,False,2,0.9,0.914,14.716998683865025,3.6220054403232314,1000
-LGBM,LGBM,observational,False,2,0.95,0.966,17.536385553259827,3.6220054403232314,1000
-LGBM,LGBM,observational,False,3,0.9,0.933,14.387779381623973,3.413493039888865,1000
-LGBM,LGBM,observational,False,3,0.95,0.977,17.144096558765057,3.413493039888865,1000
-LGBM,LGBM,observational,False,4,0.9,0.842,18.12890407178146,5.76469995432725,1000
-LGBM,LGBM,observational,False,4,0.95,0.932,21.60192157993247,5.76469995432725,1000
-LGBM,LGBM,observational,False,5,0.9,0.917,7.704461439047936,1.901354597911667,1000
-LGBM,LGBM,observational,False,5,0.95,0.96,9.18043204172422,1.901354597911667,1000
-LGBM,LGBM,observational,False,6,0.9,0.922,7.569503728862976,1.7987540722258704,1000
-LGBM,LGBM,observational,False,6,0.95,0.972,9.019620011362129,1.7987540722258704,1000
+LGBM,LGBM,observational,False,1,0.9,0.893,12.590652453419517,3.3787304616783773,1000
+LGBM,LGBM,observational,False,1,0.95,0.953,15.002687744501154,3.3787304616783773,1000
+LGBM,LGBM,observational,False,2,0.9,0.914,14.716645515368727,3.622038823656133,1000
+LGBM,LGBM,observational,False,2,0.95,0.966,17.535964727040476,3.622038823656133,1000
+LGBM,LGBM,observational,False,3,0.9,0.933,14.387879061625718,3.413516510279929,1000
+LGBM,LGBM,observational,False,3,0.95,0.977,17.14421533481309,3.413516510279929,1000
+LGBM,LGBM,observational,False,4,0.9,0.843,18.129751335736472,5.765050835726839,1000
+LGBM,LGBM,observational,False,4,0.95,0.932,21.602931157204278,5.765050835726839,1000
+LGBM,LGBM,observational,False,5,0.9,0.917,7.704465948378402,1.901185439754437,1000
+LGBM,LGBM,observational,False,5,0.95,0.96,9.180437414922883,1.901185439754437,1000
+LGBM,LGBM,observational,False,6,0.9,0.922,7.569553123736534,1.7987428999886972,1000
+LGBM,LGBM,observational,False,6,0.95,0.971,9.019678868984235,1.7987428999886972,1000
 LGBM,LGBM,observational,True,1,0.9,0.906,4.1677645580041025,1.0303464962408189,1000
 LGBM,LGBM,observational,True,1,0.95,0.967,4.966197779476664,1.0303464962408189,1000
 LGBM,LGBM,observational,True,2,0.9,0.917,5.020279353776863,1.2235227285733399,1000
diff --git a/results/did/did_pa_atte_coverage_metadata.csv b/results/did/did_pa_atte_coverage_metadata.csv
index 65f17867..1e597647 100644
--- a/results/did/did_pa_atte_coverage_metadata.csv
+++ b/results/did/did_pa_atte_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,did_pa_atte_coverage.py,2025-01-08 16:22:26,14811.1988697052,3.12.8
+0.10.dev0,did_pa_atte_coverage.py,2025-04-24 10:30:16,10597.874007225037,3.12.3
diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml
new file mode 100644
index 00000000..fa871580
--- /dev/null
+++ b/results/did/did_pa_multi_config.yml
@@ -0,0 +1,123 @@
+confidence_parameters:
+  level:
+  - 0.95
+  - 0.9
+dgp_parameters:
+  DGP:
+  - 1
+  - 4
+  - 6
+  n_obs:
+  - 2000
+dml_parameters:
+  in_sample_normalization:
+  - true
+  - false
+  learners:
+  - ml_g: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._base.LinearRegression
+      _sklearn_version: 1.5.2
+      copy_X: true
+      fit_intercept: true
+      n_jobs: null
+      positive: false
+    ml_m: !!python/tuple
+    - Linear
+    - !!python/object:sklearn.linear_model._logistic.LogisticRegression
+      C: 1.0
+      _sklearn_version: 1.5.2
+      class_weight: null
+      dual: false
+      fit_intercept: true
+      intercept_scaling: 1
+      l1_ratio: null
+      max_iter: 100
+      multi_class: deprecated
+      n_jobs: null
+      penalty: l2
+      random_state: null
+      solver: lbfgs
+      tol: 0.0001
+      verbose: 0
+      warm_start: false
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.02
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMClassifier
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.02
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+  score:
+  - observational
+  - experimental
+simulation_parameters:
+  max_runtime: 19800
+  n_jobs: -2
+  random_seed: 42
+  repetitions: 1000
diff --git a/results/did/did_pa_multi_coverage.csv b/results/did/did_pa_multi_coverage.csv
new file mode 100644
index 00000000..8276aac2
--- /dev/null
+++ b/results/did/did_pa_multi_coverage.csv
@@ -0,0 +1,25 @@
+Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
+Linear,Linear,experimental,False,1,0.9,0.8875,0.589658704307476,0.14271881238392356,0.85,0.9184713776325004,20
+Linear,Linear,experimental,False,1,0.95,0.9458333333333334,0.7026216829731943,0.14271881238392356,0.9,1.0088572704968817,20
+Linear,Linear,experimental,False,4,0.9,0.7041666666666667,2.0297642964886093,0.9047675553035617,0.55,2.9260160123442596,20
+Linear,Linear,experimental,False,4,0.95,0.7708333333333333,2.418613336188561,0.9047675553035617,0.65,3.265541044699371,20
+Linear,Linear,experimental,False,6,0.9,0.9375,1.9858835644872976,0.4370880265956636,0.9,2.8747704680291024,20
+Linear,Linear,experimental,False,6,0.95,0.975,2.3663262190076697,0.4370880265956636,0.9,3.2067165503022985,20
+Linear,Linear,experimental,True,1,0.9,0.9041666666666666,0.5893929319394263,0.1419165948760371,0.9,0.9179189354368003,20
+Linear,Linear,experimental,True,1,0.95,0.9458333333333334,0.7023049956638021,0.1419165948760371,0.9,1.0086412447991253,20
+Linear,Linear,experimental,True,4,0.9,0.7041666666666667,2.031873164323966,0.9059623323417508,0.6,2.943810800692373,20
+Linear,Linear,experimental,True,4,0.95,0.7833333333333333,2.4211262072050013,0.9059623323417508,0.6,3.2739360531515684,20
+Linear,Linear,experimental,True,6,0.9,0.9458333333333334,1.9877755956053036,0.4386853538456557,0.9,2.8801526124840118,20
+Linear,Linear,experimental,True,6,0.95,0.975,2.3685807131390373,0.4386853538456557,0.9,3.2111476829248415,20
+Linear,Linear,observational,False,1,0.9,0.9125,0.6827078489949769,0.15467502808332462,0.9,1.0590069349882765,20
+Linear,Linear,observational,False,1,0.95,0.9583333333333334,0.8134965774875249,0.15467502808332462,1.0,1.1738447754301748,20
+Linear,Linear,observational,False,4,0.9,0.8,2.7854153587870214,0.8355183226583197,0.7,4.0015833335633415,20
+Linear,Linear,observational,False,4,0.95,0.8416666666666666,3.3190271132668636,0.8355183226583197,0.8,4.483472762454467,20
+Linear,Linear,observational,False,6,0.9,0.9333333333333333,2.5337718344427866,0.5967437923241619,0.9,3.6412755250672886,20
+Linear,Linear,observational,False,6,0.95,0.9708333333333334,3.0191753595448407,0.5967437923241619,0.95,4.081839476947988,20
+Linear,Linear,observational,True,1,0.9,0.9041666666666666,0.6620805479575324,0.15571526121686724,0.85,1.0317138682400495,20
+Linear,Linear,observational,True,1,0.95,0.9541666666666666,0.7889176323040639,0.15571526121686724,0.95,1.1316182486711308,20
+Linear,Linear,observational,True,4,0.9,0.7916666666666667,2.631108576842307,0.8079463860509168,0.7,3.77565721661986,20
+Linear,Linear,observational,True,4,0.95,0.8416666666666666,3.1351592418487586,0.8079463860509168,0.75,4.24016752287414,20
+Linear,Linear,observational,True,6,0.9,0.9333333333333333,2.3368968698600474,0.5421819294976428,0.9,3.361076606239287,20
+Linear,Linear,observational,True,6,0.95,0.9791666666666666,2.784584369977626,0.5421819294976428,0.95,3.7810902213750666,20
diff --git a/results/did/did_pa_multi_coverage_metadata.csv b/results/did/did_pa_multi_coverage_metadata.csv
new file mode 100644
index 00000000..0cb88dc6
--- /dev/null
+++ b/results/did/did_pa_multi_coverage_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
+0.10.dev0,did_pa_multi_coverage.py,2025-03-18 07:37:01,120.99546647071838,3.11.9
diff --git a/results/plm/plr_ate_config.yml b/results/plm/plr_ate_config.yml
new file mode 100644
index 00000000..af73b185
--- /dev/null
+++ b/results/plm/plr_ate_config.yml
@@ -0,0 +1,386 @@
+confidence_parameters:
+  level:
+  - 0.95
+  - 0.9
+dgp_parameters:
+  dim_x:
+  - 20
+  n_obs:
+  - 500
+  theta:
+  - 0.5
+dml_parameters:
+  learners:
+  - ml_g: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+  - ml_g: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: &id001 !!python/tuple
+      - criterion
+      - max_depth
+      - min_samples_split
+      - min_samples_leaf
+      - min_weight_fraction_leaf
+      - max_features
+      - max_leaf_nodes
+      - min_impurity_decrease
+      - random_state
+      - ccp_alpha
+      - monotonic_cst
+      max_depth: 5
+      max_features: 10
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 20
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 200
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+    ml_m: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 10
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 20
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 200
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+  - ml_g: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+    ml_m: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 10
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 20
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 200
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+  - ml_g: !!python/tuple
+    - Random Forest
+    - !!python/object:sklearn.ensemble._forest.RandomForestRegressor
+      _sklearn_version: 1.5.2
+      bootstrap: true
+      ccp_alpha: 0.0
+      class_weight: null
+      criterion: squared_error
+      estimator: !!python/object:sklearn.tree._classes.DecisionTreeRegressor
+        _sklearn_version: 1.5.2
+        ccp_alpha: 0.0
+        class_weight: null
+        criterion: squared_error
+        max_depth: null
+        max_features: null
+        max_leaf_nodes: null
+        min_impurity_decrease: 0.0
+        min_samples_leaf: 1
+        min_samples_split: 2
+        min_weight_fraction_leaf: 0.0
+        monotonic_cst: null
+        random_state: null
+        splitter: best
+      estimator_params: *id001
+      max_depth: 5
+      max_features: 10
+      max_leaf_nodes: null
+      max_samples: null
+      min_impurity_decrease: 0.0
+      min_samples_leaf: 20
+      min_samples_split: 2
+      min_weight_fraction_leaf: 0.0
+      monotonic_cst: null
+      n_estimators: 200
+      n_jobs: null
+      oob_score: false
+      random_state: null
+      verbose: 0
+      warm_start: false
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.01
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.01
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+  - ml_g: !!python/tuple
+    - LGBM
+    - !!python/object:lightgbm.sklearn.LGBMRegressor
+      _Booster: null
+      _best_iteration: -1
+      _best_score: {}
+      _class_map: null
+      _class_weight: null
+      _classes: null
+      _evals_result: {}
+      _n_classes: -1
+      _n_features: -1
+      _n_features_in: -1
+      _objective: null
+      _other_params:
+        verbose: -1
+      boosting_type: gbdt
+      class_weight: null
+      colsample_bytree: 1.0
+      importance_type: split
+      learning_rate: 0.01
+      max_depth: -1
+      min_child_samples: 20
+      min_child_weight: 0.001
+      min_split_gain: 0.0
+      n_estimators: 500
+      n_jobs: 1
+      num_leaves: 31
+      objective: null
+      random_state: null
+      reg_alpha: 0.0
+      reg_lambda: 0.0
+      subsample: 1.0
+      subsample_for_bin: 200000
+      subsample_freq: 0
+      verbose: -1
+    ml_m: !!python/tuple
+    - Lasso
+    - !!python/object:sklearn.linear_model._coordinate_descent.LassoCV
+      _sklearn_version: 1.5.2
+      alphas: null
+      copy_X: true
+      cv: null
+      eps: 0.001
+      fit_intercept: true
+      max_iter: 1000
+      n_alphas: 100
+      n_jobs: null
+      positive: false
+      precompute: auto
+      random_state: null
+      selection: cyclic
+      tol: 0.0001
+      verbose: false
+  score:
+  - partialling out
+  - IV-type
+simulation_parameters:
+  max_runtime: 19800
+  n_jobs: -2
+  random_seed: 42
+  repetitions: 1000
diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv
index e1067cc0..87a505d6 100644
--- a/results/plm/plr_ate_coverage.csv
+++ b/results/plm/plr_ate_coverage.csv
@@ -1,17 +1,25 @@
-Learner g,Learner m,score,level,Coverage,CI Length,Bias,repetition
-Lasso,Lasso,IV-type,0.9,0.881,0.1393979255576113,0.0352891099128789,1000
-Lasso,Lasso,IV-type,0.95,0.945,0.16610287331091153,0.0352891099128789,1000
-Lasso,Lasso,partialling out,0.9,0.908,0.14646362984437974,0.034686755904342816,1000
-Lasso,Lasso,partialling out,0.95,0.956,0.17452217926042807,0.034686755904342816,1000
-Lasso,Random Forest,IV-type,0.9,0.895,0.14672332096879584,0.03621404411832195,1000
-Lasso,Random Forest,IV-type,0.95,0.953,0.17483162032109167,0.03621404411832195,1000
-Lasso,Random Forest,partialling out,0.9,0.816,0.14332193251141256,0.04209132271298422,1000
-Lasso,Random Forest,partialling out,0.95,0.888,0.17077861599008798,0.04209132271298422,1000
-Random Forest,Lasso,IV-type,0.9,0.883,0.14193144590758777,0.03591650651278451,1000
-Random Forest,Lasso,IV-type,0.95,0.951,0.16912174900823193,0.03591650651278451,1000
-Random Forest,Lasso,partialling out,0.9,0.901,0.1519648979095884,0.03615745065828101,1000
-Random Forest,Lasso,partialling out,0.95,0.952,0.181077344474182,0.03615745065828101,1000
-Random Forest,Random Forest,IV-type,0.9,0.893,0.14942288181113228,0.03672076481604481,1000
-Random Forest,Random Forest,IV-type,0.95,0.948,0.17804834546815554,0.03672076481604481,1000
-Random Forest,Random Forest,partialling out,0.9,0.878,0.14635785065361873,0.037417599679567926,1000
-Random Forest,Random Forest,partialling out,0.95,0.946,0.17439613558042621,0.037417599679567926,1000
+Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition
+LGBM,LGBM,IV-type,0.9,0.873,0.1600979705787658,0.041736453670531866,1000
+LGBM,LGBM,IV-type,0.95,0.937,0.19076849829726017,0.041736453670531866,1000
+LGBM,LGBM,partialling out,0.9,0.807,0.14701046434404016,0.043407336971130486,1000
+LGBM,LGBM,partialling out,0.95,0.887,0.17517377275621215,0.043407336971130486,1000
+LGBM,Lasso,IV-type,0.9,0.878,0.14908437061246727,0.03862236976140604,1000
+LGBM,Lasso,IV-type,0.95,0.926,0.17764498449616647,0.03862236976140604,1000
+LGBM,Lasso,partialling out,0.9,0.892,0.15981114790450912,0.039529461666836616,1000
+LGBM,Lasso,partialling out,0.95,0.945,0.19042672800093632,0.039529461666836616,1000
+Lasso,Lasso,IV-type,0.9,0.877,0.1404646476549414,0.03601132554422347,1000
+Lasso,Lasso,IV-type,0.95,0.928,0.16737395108830316,0.03601132554422347,1000
+Lasso,Lasso,partialling out,0.9,0.9,0.14736731076791165,0.03607425067346851,1000
+Lasso,Lasso,partialling out,0.95,0.938,0.17559898149657663,0.03607425067346851,1000
+Lasso,Random Forest,IV-type,0.9,0.839,0.13067912086907693,0.03663040619086667,1000
+Lasso,Random Forest,IV-type,0.95,0.901,0.1557137767385692,0.03663040619086667,1000
+Lasso,Random Forest,partialling out,0.9,0.753,0.14303542962976423,0.04845002977336067,1000
+Lasso,Random Forest,partialling out,0.95,0.854,0.17043722675016001,0.04845002977336067,1000
+Random Forest,Lasso,IV-type,0.9,0.883,0.14162830043619654,0.03632778967870182,1000
+Random Forest,Lasso,IV-type,0.95,0.933,0.16876052889948315,0.03632778967870182,1000
+Random Forest,Lasso,partialling out,0.9,0.888,0.1512642580778129,0.037711493804103186,1000
+Random Forest,Lasso,partialling out,0.95,0.948,0.18024248062130577,0.037711493804103186,1000
+Random Forest,Random Forest,IV-type,0.9,0.852,0.13195953745670919,0.036614704598155204,1000
+Random Forest,Random Forest,IV-type,0.95,0.911,0.15723948720656877,0.036614704598155204,1000
+Random Forest,Random Forest,partialling out,0.9,0.896,0.14282075762578683,0.03605962387417277,1000
+Random Forest,Random Forest,partialling out,0.95,0.94,0.17018142928016597,0.03605962387417277,1000
diff --git a/results/plm/plr_ate_coverage_metadata.csv b/results/plm/plr_ate_coverage_metadata.csv
index 8d5c7fd5..5d2418e3 100644
--- a/results/plm/plr_ate_coverage_metadata.csv
+++ b/results/plm/plr_ate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_ate_coverage.py,2025-01-08 14:10:27,6916.502653360367,3.12.8
+0.10.dev0,plr_ate_coverage.py,2025-04-24 09:36:42,6508.21887755394,3.12.3
diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv
new file mode 100644
index 00000000..16eb5fba
--- /dev/null
+++ b/results/plm/plr_ate_metadata.csv
@@ -0,0 +1,2 @@
+DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
+0.10.dev0,PLRATECoverageSimulation,2025-04-28 10:01,17.98821387688319,3.12.9,scripts/plm/plr_ate_config.yml
diff --git a/results/plm/plr_ate_sensitivity.csv b/results/plm/plr_ate_sensitivity.csv
index 00934b4f..7981ab2d 100644
--- a/results/plm/plr_ate_sensitivity.csv
+++ b/results/plm/plr_ate_sensitivity.csv
@@ -1,17 +1,17 @@
 Learner g,Learner m,score,level,Coverage,CI Length,Bias,Coverage (Lower),Coverage (Upper),RV,RVa,Bias (Lower),Bias (Upper),repetition
 LGBM,LGBM,IV-type,0.9,0.49,1.2836841849471865,0.642852034975727,1.0,0.998,0.0883267598773138,0.025200860015212666,1.3451446703513257,0.27137365708627864,500
-LGBM,LGBM,IV-type,0.95,0.65,1.529604050351386,0.642852034975727,1.0,1.0,0.0883267598773138,0.013790413915027113,1.3451446703513257,0.27137365708627864,500
+LGBM,LGBM,IV-type,0.95,0.65,1.529604050351386,0.642852034975727,1.0,1.0,0.0883267598773138,0.013790413915027114,1.3451446703513257,0.27137365708627864,500
 LGBM,LGBM,partialling out,0.9,0.052,1.0555526522517718,0.9216896766797483,1.0,0.878,0.12290629723561408,0.0667859903596657,1.6463724503661545,0.2830593598175856,500
 LGBM,LGBM,partialling out,0.95,0.114,1.2577685626857555,0.9216896766797483,1.0,0.962,0.12290629723561408,0.05210975377693211,1.6463724503661545,0.2830593598175856,500
-LGBM,Random Forest,IV-type,0.9,0.072,1.1846157067784349,0.9322070273917288,1.0,0.934,0.11754241223304673,0.058515658188839345,1.6980814469560257,0.26726499739118403,500
-LGBM,Random Forest,IV-type,0.95,0.16,1.4115566776050241,0.9322070273917288,1.0,0.994,0.11754241223304673,0.04334326180178758,1.6980814469560257,0.26726499739118403,500
-LGBM,Random Forest,partialling out,0.9,0.078,1.2415314353647366,0.9950962660593035,1.0,0.918,0.11842149116467734,0.06021024644530299,1.8099982510012884,0.29229694861359934,500
-LGBM,Random Forest,partialling out,0.95,0.15,1.479375951220122,0.9950962660593035,1.0,0.98,0.11842149116467734,0.04525082945770369,1.8099982510012884,0.29229694861359934,500
-Random Forest,LGBM,IV-type,0.9,0.554,1.900486623597139,0.8881666888956794,1.0,1.0,0.07201385567825232,0.01814220891090863,2.11967639571274,0.46266796209857214,500
-Random Forest,LGBM,IV-type,0.95,0.746,2.2645694877143114,0.8881666888956794,1.0,1.0,0.07201385567825232,0.008578785415219068,2.11967639571274,0.46266796209857214,500
-Random Forest,LGBM,partialling out,0.9,0.002,1.5126486531347854,1.573242725840773,1.0,0.818,0.12824967172299567,0.08055028818351519,2.774465314925881,0.4031005519125824,500
-Random Forest,LGBM,partialling out,0.95,0.008,1.8024320418722997,1.573242725840773,1.0,0.948,0.12824967172299567,0.06705003584560411,2.774465314925881,0.4031005519125824,500
-Random Forest,Random Forest,IV-type,0.9,0.012,1.7129890288584426,1.6194855098925158,1.0,0.892,0.11959162432568951,0.07060214596391444,2.948463222220921,0.39969580126755805,500
-Random Forest,Random Forest,IV-type,0.95,0.038,2.041152323503278,1.6194855098925158,1.0,0.972,0.11959162432568951,0.05677408319758889,2.948463222220921,0.39969580126755805,500
-Random Forest,Random Forest,partialling out,0.9,0.0,1.7381117353695532,1.7366675645551972,1.0,0.824,0.1281371703134689,0.07833372270549209,3.0610973455175285,0.4635968018257515,500
-Random Forest,Random Forest,partialling out,0.95,0.018,2.0710878746970973,1.7366675645551972,1.0,0.946,0.1281371703134689,0.0642547167440049,3.0610973455175285,0.4635968018257515,500
+LGBM,Random Forest,IV-type,0.9,0.076,1.1831625247072215,0.9308062239804228,1.0,0.946,0.11734274159129685,0.05847446162225632,1.6971412413918896,0.26673626718612453,500
+LGBM,Random Forest,IV-type,0.95,0.15,1.40982510436599,0.9308062239804228,1.0,0.99,0.11734274159129685,0.04330052167462019,1.6971412413918896,0.26673626718612453,500
+LGBM,Random Forest,partialling out,0.9,0.074,1.2408824102177636,0.9969465002467008,1.0,0.93,0.11861197624692194,0.06042868914339051,1.8119621159758381,0.29323133175754634,500
+LGBM,Random Forest,partialling out,0.95,0.148,1.4786025900575936,0.9969465002467008,1.0,0.978,0.11861197624692194,0.04546020549453332,1.8119621159758381,0.29323133175754634,500
+Random Forest,LGBM,IV-type,0.9,0.556,1.9014670092481476,0.8897746033367344,1.0,1.0,0.07211930902619489,0.018402978138876647,2.1220916883044794,0.46567125428407596,500
+Random Forest,LGBM,IV-type,0.95,0.754,2.26573768927064,0.8897746033367344,1.0,1.0,0.07211930902619489,0.008809037433830246,2.1220916883044794,0.46567125428407596,500
+Random Forest,LGBM,partialling out,0.9,0.002,1.5148779135183594,1.5704287430339756,1.0,0.828,0.12800123432195906,0.0802383681199541,2.771929499160999,0.4014709332877519,500
+Random Forest,LGBM,partialling out,0.95,0.01,1.805088369458156,1.5704287430339756,1.0,0.942,0.12800123432195906,0.06673188982925375,2.771929499160999,0.4014709332877519,500
+Random Forest,Random Forest,IV-type,0.9,0.008,1.712740468910975,1.6062309195696611,1.0,0.896,0.11862651936611456,0.06965279743071341,2.9355183176965993,0.3908974740734249,500
+Random Forest,Random Forest,IV-type,0.95,0.042,2.040856146058031,1.6062309195696611,1.0,0.972,0.11862651936611456,0.05582930384773342,2.9355183176965993,0.3908974740734249,500
+Random Forest,Random Forest,partialling out,0.9,0.002,1.7398328179227547,1.738765052841725,1.0,0.826,0.12823635224819166,0.07839810366339917,3.0635620106955765,0.4647217397313531,500
+Random Forest,Random Forest,partialling out,0.95,0.016,2.0731386710496866,1.738765052841725,1.0,0.942,0.12823635224819166,0.06431405001184519,3.0635620106955765,0.4647217397313531,500
diff --git a/results/plm/plr_ate_sensitivity_metadata.csv b/results/plm/plr_ate_sensitivity_metadata.csv
index f0d1e744..18d033f5 100644
--- a/results/plm/plr_ate_sensitivity_metadata.csv
+++ b/results/plm/plr_ate_sensitivity_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_ate_sensitivity.py,2025-01-08 16:36:12,15659.224347829819,3.12.8
+0.10.dev0,plr_ate_sensitivity.py,2025-04-24 10:27:15,9539.846628665924,3.12.3
diff --git a/results/plm/plr_cate_coverage_metadata.csv b/results/plm/plr_cate_coverage_metadata.csv
index 59b490d4..9e81be87 100644
--- a/results/plm/plr_cate_coverage_metadata.csv
+++ b/results/plm/plr_cate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_cate_coverage.py,2025-01-08 14:25:31,7808.868787527084,3.12.8
+0.10.dev0,plr_cate_coverage.py,2025-04-24 08:56:30,4092.604692697525,3.12.3
diff --git a/results/plm/plr_gate_coverage_metadata.csv b/results/plm/plr_gate_coverage_metadata.csv
index febe93f4..980af1a1 100644
--- a/results/plm/plr_gate_coverage_metadata.csv
+++ b/results/plm/plr_gate_coverage_metadata.csv
@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.10.dev0,plr_gate_coverage.py,2025-01-08 13:02:47,2835.5379569530487,3.12.8
+0.10.dev0,plr_gate_coverage.py,2025-04-24 08:14:37,1583.488787651062,3.12.3
diff --git a/scripts/did/did_cs_atte_coverage.py b/scripts/did/did_cs_atte_coverage.py
index 3886ea8e..9be682bc 100644
--- a/scripts/did/did_cs_atte_coverage.py
+++ b/scripts/did/did_cs_atte_coverage.py
@@ -7,7 +7,7 @@
 from lightgbm import LGBMRegressor, LGBMClassifier
 
 import doubleml as dml
-from doubleml.datasets import make_did_SZ2020
+from doubleml.did.datasets import make_did_SZ2020
 
 # Number of repetitions
 n_rep = 1000
diff --git a/scripts/did/did_pa_atte_coverage.py b/scripts/did/did_pa_atte_coverage.py
index 5184860d..144597a1 100644
--- a/scripts/did/did_pa_atte_coverage.py
+++ b/scripts/did/did_pa_atte_coverage.py
@@ -7,7 +7,7 @@
 from lightgbm import LGBMRegressor, LGBMClassifier
 
 import doubleml as dml
-from doubleml.datasets import make_did_SZ2020
+from doubleml.did.datasets import make_did_SZ2020
 
 # Number of repetitions
 n_rep = 1000
diff --git a/scripts/did/did_pa_multi.py b/scripts/did/did_pa_multi.py
new file mode 100644
index 00000000..f06a03e4
--- /dev/null
+++ b/scripts/did/did_pa_multi.py
@@ -0,0 +1,14 @@
+
+from montecover.did import DIDMultiCoverageSimulation
+
+# Create and run simulation with config file
+sim = DIDMultiCoverageSimulation(
+    config_file="scripts/did/did_pa_multi_config.yml",
+    log_level="DEBUG",
+    log_file="logs/did/did_pa_multi_sim.log"
+)
+sim.run_simulation()
+sim.save_results(output_path="results/did/", file_prefix="did_multi")
+
+# Save config file for reproducibility
+sim.save_config("results/did/did_pa_multi_config.yml")
diff --git a/scripts/did/did_pa_multi_config.yml b/scripts/did/did_pa_multi_config.yml
new file mode 100644
index 00000000..67eead13
--- /dev/null
+++ b/scripts/did/did_pa_multi_config.yml
@@ -0,0 +1,28 @@
+# Simulation parameters for DID Multi Coverage
+
+simulation_parameters:
+  repetitions: 1000
+  max_runtime: 19800 # 5.5 hours in seconds
+  random_seed: 42
+  n_jobs: -2
+
+dgp_parameters:
+  DGP: [1, 4, 6] # Different DGP specifications
+  n_obs: [2000] # Sample size for each simulation (has to be a list)
+
+dml_parameters:
+  # ML methods for ml_g and ml_m
+  learners:
+    - ml_g: ["Linear"]
+      ml_m: ["Linear"]
+    - ml_g: ["LGBM"]
+      ml_m: ["LGBM"]
+
+  score:
+    - observational # Standard DML score
+    - experimental # Experimental score (no propensity estimation)
+
+  in_sample_normalization: [true, false]
+
+confidence_parameters:
+  level: [0.95, 0.90] # Confidence levels
diff --git a/scripts/plm/plr_ate.py b/scripts/plm/plr_ate.py
new file mode 100644
index 00000000..09e25312
--- /dev/null
+++ b/scripts/plm/plr_ate.py
@@ -0,0 +1,14 @@
+
+from montecover.plm import PLRATECoverageSimulation
+
+# Create and run simulation with config file
+sim = PLRATECoverageSimulation(
+    config_file="scripts/plm/plr_ate_config.yml",
+    log_level="INFO",
+    log_file="logs/plm/plr_ate_sim.log"
+)
+sim.run_simulation()
+sim.save_results(output_path="results/plm/", file_prefix="plr_ate")
+
+# Save config file for reproducibility
+sim.save_config("results/plm/plr_ate_config.yml")
diff --git a/scripts/plm/plr_ate_config.yml b/scripts/plm/plr_ate_config.yml
new file mode 100644
index 00000000..e2067bc1
--- /dev/null
+++ b/scripts/plm/plr_ate_config.yml
@@ -0,0 +1,33 @@
+# Simulation parameters for PLR ATE Coverage
+
+simulation_parameters:
+  repetitions: 1000
+  max_runtime: 19800 # 5.5 hours in seconds
+  random_seed: 42
+  n_jobs: -2
+
+dgp_parameters:
+  theta: [0.5] # Treatment effect
+  n_obs: [500] # Sample size
+  dim_x: [20] # Number of covariates
+
+dml_parameters:
+  # ML methods for ml_g and ml_m
+  learners:
+    - ml_g: ["Lasso"]
+      ml_m: ["Lasso"]
+    - ml_g: ["Random Forest"]
+      ml_m: ["Random Forest"]
+    - ml_g: ["Lasso"]
+      ml_m: ["Random Forest"]
+    - ml_g: ["Random Forest"]
+      ml_m: ["Lasso"]
+    - ml_g: ["LGBM"]
+      ml_m: ["LGBM"]
+    - ml_g: ["LGBM"]
+      ml_m: ["Lasso"]
+
+  score: ["partialling out", "IV-type"]
+
+confidence_parameters:
+  level: [0.95, 0.90] # Confidence levels
\ No newline at end of file