Merge pull request #96 from DoubleML/m-nonlinear-score-mixin

Add documentation for the non-linear score mixin class

Author: PhilippBach

.gitignore

@@ -1 +1,2 @@
 *.idea
+

.idea/.gitignore

@@ -56,4 +56,16 @@ Dataset generators
    datasets.make_irm_data
    datasets.make_iivm_data
    datasets.make_plr_turrell2018
-   datasets.make_pliv_multiway_cluster_CKMS2021
+   datasets.make_pliv_multiway_cluster_CKMS2021
+
+Score mixin classes for double machine learning models
+------------------------------------------------------
+
+.. currentmodule:: doubleml
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    double_ml_score_mixins.LinearScoreMixin
+    double_ml_score_mixins.NonLinearScoreMixin

.idea/doubleml-docs.iml

@@ -129,6 +129,7 @@
     'https://doi.org/10.1111/ectj.12097',  # Valid DOI; Causes 403 Client Error: Forbidden for url:...
     'https://doi.org/10.2307/2171802',  # Valid DOI; Causes 403 Client Error: Forbidden for url:...
     'https://doi.org/10.2307/1912705',  # Valid DOI; Causes 403 Client Error: Forbidden for url:...
+    'https://doi.org/10.1093/ectj/utaa027', # Valid DOI; Causes 403 Client Error: Forbidden for url:...
 ]
 
 # To execute R code via jupyter-execute one needs to install the R kernel for jupyter

.idea/inspectionProfiles/profiles_settings.xml

@@ -1141,7 +1141,7 @@
    },
    "source": [
     "These estimates that flexibly account for confounding are\n",
-    "substantially attenuated relative to the baseline estimate (*19559*) that does not account for confounding. They suggest much smaller causal effects of 401(k) eligiblity on financial asset holdings. The best model with lowest RMSE in both equations is the PLR model estimated via lasso. It gives the following estimate:"
+    "substantially attenuated relative to the baseline estimate (*19559*) that does not account for confounding. They suggest much smaller causal effects of 401(k) eligiblity on financial asset holdings."
    ]
   },
   {

.idea/misc.xml

@@ -25,6 +25,8 @@ These are case studies with the Python package :ref:`DoubleML <doubleml_package>
 
     py_double_ml_pension.ipynb
     py_double_ml_multiway_cluster.ipynb
+    py_double_ml_gate.ipynb
+    py_double_ml_cate.ipynb
 
 |start-h3| Sandbox |end-h3|