update guide

Author: SvenKlaassen

doc/guide/models/plm/lplr.rst

@@ -5,7 +5,7 @@
     \mathbb{E} [Y | D, X] = \mathbb{P} (Y=1 | D, X) = \text{expit} \{\beta_0 D + r_0 (X) \}
 
 where :math:`Y` is the binary outcome variable and :math:`D` is the policy variable of interest.
-The high-dimensional vector :math:`X = (X_1, \ldots, X_p)` consists of other confounding covariates.
+The high-dimensional vector :math:`X = (X_1, \ldots, X_p)` consists of confounding covariates and
 :math:`\text{expit}` is the logistic link function
 
 .. math::

doc/guide/models/plm/plm_models.inc

@@ -64,6 +64,40 @@ Partially linear regression model (PLR)
             dml_plr_obj$fit()
             print(dml_plr_obj)
 
+.. _lplr-model:
+
+Logistic partially linear regression model (LPLR)
+*************************************************
+
+.. include:: /guide/models/plm/lplr.rst
+
+.. include:: /shared/causal_graphs/plr_irm_causal_graph.rst
+
+``DoubleMLLPLR`` implements LPLR models. Estimation is conducted via its ``fit()`` method.
+
+.. note::
+    Remark that the treatment effects are not additive in this model. The partial linear term enters the model through a logistic link function.
+
+.. tab-set::
+
+    .. tab-item:: Python
+        :sync: py
+
+        .. ipython:: python
+
+            import numpy as np
+            import doubleml as dml
+            from doubleml.plm.datasets import make_lplr_LZZ2020
+            from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+            from sklearn.base import clone
+            np.random.seed(3141)
+            ml_t = RandomForestRegressor(n_estimators=100, max_features=15, max_depth=15, min_samples_leaf=5)
+            ml_m = RandomForestRegressor(n_estimators=100, max_features=15, max_depth=15, min_samples_leaf=5)
+            ml_M = RandomForestClassifier(n_estimators=100, max_features=15, max_depth=15, min_samples_leaf=5)
+            obj_dml_data = make_lplr_LZZ2020(alpha=0.5, n_obs=1000, dim_x=15)
+            dml_lplr_obj = dml.DoubleMLLPLR(obj_dml_data, ml_M, ml_t, ml_m)
+            dml_lplr_obj.fit().summary
+
 
 .. _pliv-model:
 
@@ -91,12 +125,12 @@ Estimation is conducted via its ``fit()`` method:
             from sklearn.ensemble import RandomForestRegressor
             from sklearn.base import clone
 
-            learner = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
+            learner = RandomForestRegressor(n_estimators=100, max_features=5, max_depth=5, min_samples_leaf=5)
             ml_l = clone(learner)
             ml_m = clone(learner)
             ml_r = clone(learner)
             np.random.seed(2222)
-            data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=20, dim_z=1, return_type='DataFrame')
+            data = make_pliv_CHS2015(alpha=0.5, n_obs=500, dim_x=5, dim_z=1, return_type='DataFrame')
             obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='Z1')
             dml_pliv_obj = dml.DoubleMLPLIV(obj_dml_data, ml_l, ml_m, ml_r)
             print(dml_pliv_obj.fit())
@@ -121,39 +155,3 @@ Estimation is conducted via its ``fit()`` method:
             dml_pliv_obj = DoubleMLPLIV$new(obj_dml_data, ml_l, ml_m, ml_r)
             dml_pliv_obj$fit()
             print(dml_pliv_obj)
-
-Logistic partially linear regression model (LPLR)
-*************************************************
-
-.. include:: /guide/models/plm/lplr.rst
-
-.. include:: /shared/causal_graphs/plr_irm_causal_graph.rst
-
-``DoubleMLLPLR`` implements LPLR models. Estimation is conducted via its ``fit()`` method.
-
-.. note::
-    Remark that the treatment effects are not additive in this model. The partial linear term enters the model through a logistic link function.
-
-.. tab-set::
-
-    .. tab-item:: Python
-        :sync: py
-
-        .. ipython:: python
-            :okwarning:
-
-            import numpy as np
-            import doubleml as dml
-            from doubleml.plm.datasets import make_lplr_LZZ2020
-            from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-            from sklearn.base import clone
-            np.random.seed(3141)
-            ml_t = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
-            ml_m = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
-            ml_M = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)
-            obj_dml_data = make_lplr_LZZ2020(alpha=0.5, n_obs=500, dim_x=20)
-            dml_lplr_obj = dml.DoubleMLPLR(obj_dml_data, ml_M, ml_t, ml_m)
-            dml_lplr_obj.fit().summary
-
-
-

doc/guide/scores/plm/lplr_score.rst

@@ -1,28 +1,28 @@
 For the LPLR model implemented in ``DoubleMLLPLR`` one can choose between
-``score='nuisance_space'`` and ``score='instrument'``. For the LPLR model let ``treatment=d`` and :math:`Y\in\{0,1\}`
+``score='nuisance_space'`` and ``score='instrument'``.
 
 ``score='nuisance_space'`` implements the score function:
 
 .. math::
 
     \psi(W, \beta, \eta) := \psi(X) \{Y e^{\beta D} -(1-Y)e^{r_0(X)} \} \{ D - m_0(X)\}
 
-with :math:`\eta = { r(\cdot), m(\cdot), \psi(\cdot) }`, where
+with nuisance elements :math:`\eta = { r(\cdot), m(\cdot), \psi(\cdot) }`, where
 
 .. math::
 
-    \r_0(X) = t_0(X) - \breve \beta a_0(X),
+    r_0(X) = t_0(X) - \breve \beta a_0(X),
 
     m_0(X) = \mathbb{E} [D | X, Y=0],
 
     \psi(X) = \text{expit} (-r_0(X)).
 
 For the estimation of :math:`r_0(X)`, we further need to obtain the following estimates as well as a preliminary estimate
-:math:`\breve \beta` as described in `Liu et al. (2021) <https://academic.oup.com/ectj/article/24/3/559/6296639>`_
+:math:`\breve \beta` as described in `Liu et al. (2021) <https://doi.org/10.1093/ectj/utab019>`_
 
 .. math::
 
-    t_0(X) = \mathbb{E} [\text{logit} M (D, X) | X],
+    t_0(X) = \mathbb{E} [\text{logit}(M (D, X)) | X],
 
     a_0(X) = \mathbb{E} [D | X].
 
@@ -32,14 +32,14 @@ For the estimation of :math:`r_0(X)`, we further need to obtain the following es
 
 .. math::
 
-    \psi(W; \beta, \eta) &:=  \mathbb E [ \{Y - \text{expit} (\beta_0 D + r_0(X )) \} Z_0 ]
+    \psi(W; \beta, \eta) :=  \mathbb E [ \{Y - \text{expit} (\beta_0 D + r_0(X )) \} Z_0 ]
 
 
 with :math:`Z_0=D-m(X)` and :math:`\eta = { r(\cdot), m(\cdot), \psi(\cdot) }`, where
 
 .. math::
 
-    \r_0(X) = t_0(X) - \breve \beta a_0(X),
+    r_0(X) = t_0(X) - \breve \beta a_0(X),
 
     m_0(X) = \mathbb{E} [D | X].