Merge pull request #187 from DoubleML/dev

Update documentation for Release 0.8.2

Author: SvenKlaassen

doc/_static/dag_usecase_revised.png

@@ -105,6 +105,7 @@
     'examples/py_double_ml_basic_iv': '_static/basic_iv_example_nb.png',
     'examples/R_double_ml_basic_iv': '_static/basic_iv_example_nb.png',
     'examples/py_double_ml_ssm': '_static/ssm_example_nb.svg',
+    'examples/py_double_ml_sensitivity_booking': '_static/dag_usecase_revised.png',
 }
 
 copybutton_prompt_text = r'>>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: | {2,5}\.\.\.\.:'

doc/conf.py

@@ -24,6 +24,7 @@ General Examples
     py_double_ml_firststage.ipynb
     py_double_ml_multiway_cluster.ipynb
     py_double_ml_ssm.ipynb
+    py_double_ml_sensitivity_booking.ipynb
     py_double_ml_did.ipynb
     py_double_ml_did_pretest.ipynb
     py_double_ml_basic_iv.ipynb

doc/examples/figures/dag_usecase_revised.png

@@ -301,7 +301,7 @@
     "\n",
     "All the previous steps of estimation are performed automatically by the `DoubleMLIRM` class if the score is set to `'ATTE'`.\n",
     "\n",
-    "**Remark**: This requires the `weights` argument to be binary and refer to the group indicator $1\\{X\\in G\\} = 1\\{X_0 \\ge 0.5\\}$ not the actual group of treated individuals $1\\{D = 1, X\\in G\\} = D\\cdot 1\\{X\\in G\\} $.\n",
+    "**Remark**: This requires the `weights` argument to be binary and refer to the group indicator $1\\{X\\in G\\} = 1\\{X_0 \\ge 0.5\\}$ not the actual group of treated individuals $1\\{D = 1, X\\in G\\} = D\\cdot 1\\{X\\in G\\}$.\n",
     "Further, the normalization by the group probabilities is then performed automatically by the `DoubleMLIRM` class.\n",
     "\n",
     "Consequently, we can just set `weights` to the group indicator $1\\{X\\in G\\}$ and fit the model."

doc/examples/index.rst

@@ -192,7 +192,8 @@ Evaluate learners
 #################
 
 To compare different learners it is possible to evaluate the out-of-sample performance of each learner. The ``summary``
-already shows the root mean squared error (RMSE) for each learner and each corresponding repetition of cross-fitting (``n_rep`` argument).
+already displays either the root-mean-squared error (for regressions) or log-loss (for classifications) for each learner
+and each corresponding repetition of cross-fitting (``n_rep`` argument).
 
 To illustrate the parameter tuning, we work with the following example.
 
@@ -216,9 +217,9 @@ To illustrate the parameter tuning, we work with the following example.
         dml_plr_obj.fit()
         print(dml_plr_obj)
 
-The RMSEs of each learner are also stored in the ``rmses`` attribute.
+The loss of each learner are also stored in the ``nuisance_loss`` attribute.
 Further, the ``evaluate_learners()`` method allows to evalute customized evaluation metrics as e.g. the mean absolute error. 
-The default option is still the RMSE for evaluation.
+The default option is still the root-mean-squared error for evaluation.
 
 .. tab-set::
 
@@ -227,7 +228,7 @@ The default option is still the RMSE for evaluation.
 
     .. ipython:: python
 
-        print(dml_plr_obj.rmses)
+        print(dml_plr_obj.nuisance_loss)
         print(dml_plr_obj.evaluate_learners())
 
 To evaluate a customized metric one has to define a ``callable``. For some models (e.g. the IRM model) it is important that

doc/examples/py_double_ml_gate_sensitivity.ipynb

@@ -7,6 +7,37 @@ Release notes
 
   .. tab-item:: Python
 
+    .. dropdown:: DoubleML 0.8.2
+      :class-title: sd-bg-primary sd-font-weight-bold
+      :open:
+
+      - **API Update**: Change nuisance evaluation for classifiers.
+        The corresponding properties are renamed ``nuisance_loss`` instead of ``rmses``.
+        `#254 <https://github.com/DoubleML/doubleml-for-py/pull/254>`_
+        `#184 <https://github.com/DoubleML/doubleml-docs/pull/184>`_
+
+      - Add new example on sensitivity analysis
+        `#190 <https://github.com/DoubleML/doubleml-docs/pull/190>`_
+
+      - Add a new example on DiD with DoubleML in R
+        `#178 <https://github.com/DoubleML/doubleml-docs/pull/178>`_
+
+      - Enable ``set_sample_splitting`` for cluster data
+        `#255 <https://github.com/DoubleML/doubleml-for-py/pull/255>`_
+
+      - Update the ``make_confounded_irm_data`` data generating process
+        `#263 <https://github.com/DoubleML/doubleml-for-py/pull/263>`_
+      
+      - Maintainance package
+        `#264 <https://github.com/DoubleML/doubleml-for-py/pull/264>`_
+
+      - Maintenance documentation
+        `#177 <https://github.com/DoubleML/doubleml-docs/pull/177>`_
+        `#180 <https://github.com/DoubleML/doubleml-docs/pull/180>`_
+        `#181 <https://github.com/DoubleML/doubleml-docs/pull/181>`_
+        `#187 <https://github.com/DoubleML/doubleml-docs/pull/187>`_
+        `#189 <https://github.com/DoubleML/doubleml-docs/pull/189>`_
+
     .. dropdown:: DoubleML 0.8.1
       :class-title: sd-bg-primary sd-font-weight-bold
       :open: