Standardize section titles to "Coverage" in multiple documentation files

Author: SvenKlaassen

doc/plm/lplr.qmd

@@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table
 init_notebook_mode(all_interactive=True)
 ```
 
-## ATE Coverage
+## Coverage
 
 The simulations are based on the  the [make_lplr_LZZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_lplr_LZZ2020.html)-DGP with $500$ observations.
 

doc/plm/pliv.qmd

@@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table
 init_notebook_mode(all_interactive=True)
 ```
 
-## LATE Coverage
+## Coverage
 
-The simulations are based on the  the [make_pliv_CHS2015](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_pliv_CHS2015.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso is a nearly optimal choice for the nuisance estimation.
+The simulations are based on the  the [make_pliv_CHS2015](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_pliv_CHS2015.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso is a nearly optimal choice for the nuisance estimation.
 
 ::: {.callout-note title="Metadata"  collapse="true"}
 

doc/plm/plr.qmd

@@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table
 init_notebook_mode(all_interactive=True)
 ```
 
-## ATE Coverage
+## Coverage
 
-The simulations are based on the  the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations.
+The simulations are based on the  the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations.
 
 ::: {.callout-note title="Metadata"  collapse="true"}
 
@@ -113,9 +113,9 @@ generate_and_show_styled_table(
 )
 ```
 
-## ATE Sensitivity
+## Sensitivity
 
-The simulations are based on the  the [make_confounded_plr_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_confounded_plr_data.html)-DGP with $1000$ observations as highlighted in the [Example Gallery](https://docs.doubleml.org/stable/examples/py_double_ml_sensitivity.html#). As the DGP is nonlinear, we will only use corresponding learners. Since the DGP includes unobserved confounders, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter.
+The simulations are based on the  the [make_confounded_plr_data](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_confounded_plr_data.html)-DGP with $1000$ observations as highlighted in the [Example Gallery](https://docs.doubleml.org/stable/examples/py_double_ml_sensitivity.html#). As the DGP is nonlinear, we will only use corresponding learners. Since the DGP includes unobserved confounders, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter.
 
 Both sensitivity parameters are set to $cf_y=cf_d=0.1$, such that the robustness value $RV$ should be approximately $10\%$.
 Further, the corresponding confidence intervals are one-sided (since the direction of the bias is unkown), such that only one side should approximate the corresponding coverage level (here only the upper coverage is relevant since the bias is positive). Remark that for the coverage level the value of $\rho$ has to be correctly specified, such that the coverage level will be generally (significantly) larger than the nominal level under the conservative choice of $|\rho|=1$.
@@ -208,3 +208,50 @@ generate_and_show_styled_table(
     coverage_highlight_cols=["Coverage", "Coverage (Upper)"]
 )
 ```
+
+## Tuning
+
+The simulations are based on the  the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration.
+
+```{python}
+#| echo: false
+
+# set up data
+df_tune_cov = pd.read_csv("../../results/plm/plr_ate_tune_coverage.csv", index_col=None)
+
+assert df_tune_cov["repetition"].nunique() == 1
+n_rep_tune_cov = df_tune_cov["repetition"].unique()[0]
+
+display_columns_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage",]
+```
+
+
+### Partialling out
+
+```{python}
+# | echo: false
+
+generate_and_show_styled_table(
+    main_df=df_tune_cov,
+    filters={"level": 0.95, "Score": "partialling out"},
+    display_cols=display_columns_tune_cov,
+    n_rep=n_rep_tune_cov,
+    level_col="level",
+    rename_map={"Learner g": "Learner l"},
+    coverage_highlight_cols=["Coverage"]
+)
+```
+
+```{python}
+#| echo: false
+
+generate_and_show_styled_table(
+    main_df=df_tune_cov,
+    filters={"level": 0.9, "Score": "partialling out"},
+    display_cols=display_columns_tune_cov,
+    n_rep=n_rep_tune_cov,
+    level_col="level",
+    rename_map={"Learner g": "Learner l"},
+    coverage_highlight_cols=["Coverage"]
+)
+```

doc/plm/plr_cate.qmd

@@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table
 init_notebook_mode(all_interactive=True)
 ```
 
-## CATE Coverage
+## Coverage
 
-The simulations are based on the  the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints).
+The simulations are based on the  the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints).
 
 The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals).
 

doc/plm/plr_gate.qmd

@@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table
 init_notebook_mode(all_interactive=True)
 ```
 
-## GATE Coverage
+## Coverage
 
-The simulations are based on the  the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints).
+The simulations are based on the  the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints).
 
 The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals).