update DoubleMLQTE with basic framework

Author: SvenKlaassen

doubleml/double_ml.py

@@ -44,7 +44,7 @@ def __init__(self,
             self._is_cluster_data = True
         self._dml_data = obj_dml_data
 
-        # initialize framework which is set after the fit method is called
+        # initialize framework which is constructed after the fit method is called
         self._framework = None
 
         # initialize learners and parameters which are set model specific

doubleml/irm/qte.py

@@ -10,6 +10,7 @@
 from .pq import DoubleMLPQ
 from .lpq import DoubleMLLPQ
 from .cvar import DoubleMLCVAR
+from ..double_ml_framework import concat
 
 from ..utils._estimation import _draw_weights, _default_kde
 from ..utils.resampling import DoubleMLResampling
@@ -130,6 +131,9 @@ def __init__(self,
             self._is_cluster_data = True
         self._check_data(self._dml_data)
 
+        # initialize framework which is constructed after the fit method is called
+        self._framework = None
+
         # initialize and check trimming
         self._trimming_rule = trimming_rule
         self._trimming_threshold = trimming_threshold
@@ -180,6 +184,28 @@ def n_rep(self):
         """
         return self._n_rep
 
+    @property
+    def n_rep_boot(self):
+        """
+        The number of bootstrap replications.
+        """
+        if self._framework is None:
+            n_rep_boot = None
+        else:
+            n_rep_boot = self._framework.n_rep_boot
+        return n_rep_boot
+
+    @property
+    def boot_method(self):
+        """
+        The method to construct the bootstrap replications.
+        """
+        if self._framework is None:
+            method = None
+        else:
+            method = self._framework.boot_method
+        return method
+
     @property
     def smpls(self):
         """
@@ -191,6 +217,13 @@ def smpls(self):
             raise ValueError(err_msg)
         return self._smpls
 
+    @property
+    def framework(self):
+        """
+        The corresponding :class:`doubleml.DoubleMLFramework` object.
+        """
+        return self._framework
+
     @property
     def quantiles(self):
         """
@@ -247,6 +280,10 @@ def coef(self):
         """
         return self._coef
 
+    @coef.setter
+    def coef(self, value):
+        self._coef = value
+
     @property
     def all_coef(self):
         """
@@ -261,6 +298,10 @@ def se(self):
         """
         return self._se
 
+    @se.setter
+    def se(self, value):
+        self._se = value
+
     @property
     def t_stat(self):
         """
@@ -277,26 +318,16 @@ def pval(self):
         pval = 2 * norm.cdf(-np.abs(self.t_stat))
         return pval
 
-    @property
-    def n_rep_boot(self):
-        """
-        The number of bootstrap replications.
-        """
-        return self._n_rep_boot
-
-    @property
-    def boot_coef(self):
-        """
-        Bootstrapped coefficients for the causal parameter(s) after calling :meth:`fit` and :meth:`bootstrap`.
-        """
-        return self._boot_coef
-
     @property
     def boot_t_stat(self):
         """
         Bootstrapped t-statistics for the causal parameter(s) after calling :meth:`fit` and :meth:`bootstrap`.
         """
-        return self._boot_t_stat
+        if self._framework is None:
+            boot_t_stat = None
+        else:
+            boot_t_stat = self._framework.boot_t_stat
+        return boot_t_stat
 
     @property
     def modellist_0(self):
@@ -393,12 +424,18 @@ def fit(self, n_jobs_models=None, n_jobs_cv=None, store_predictions=True, store_
                                  for i_quant in range(self.n_quantiles))
 
         # combine the estimates and scores
+        framework_list = [None] * self.n_quantiles
+
         for i_quant in range(self.n_quantiles):
             self._i_quant = i_quant
             # save the parallel fitted models in the right list
             self._modellist_0[self._i_quant] = fitted_models[self._i_quant][0]
             self._modellist_1[self._i_quant] = fitted_models[self._i_quant][1]
 
+            # set up the framework
+            framework_list[self._i_quant] = self._modellist_1[self._i_quant].framework - \
+                self._modellist_0[self._i_quant].framework
+
             # treatment Effects
             self._all_coef[self._i_quant, :] = self.modellist_1[self._i_quant].all_coef - \
                 self.modellist_0[self._i_quant].all_coef
@@ -419,6 +456,9 @@ def fit(self, n_jobs_models=None, n_jobs_cv=None, store_predictions=True, store_
         # aggregated parameter estimates and standard errors from repeated cross-fitting
         self._agg_cross_fit()
 
+        # aggregate all frameworks
+        self._framework = concat(framework_list)
+
         return self
 
     def bootstrap(self, method='normal', n_rep_boot=500):