working sampler

dowhy/api/causal_data_frame.py

@@ -27,10 +27,10 @@ class CausalAccessor(object):
 
     def do(self, x, method=None, num_cores=1, variable_types={}, outcome=None, params=None, dot_graph=None,
            common_causes=None, instruments=None, estimand_type='ate', proceed_when_unidentifiable=False,
-           keep_original_treatment=False):
+           keep_original_treatment=False, use_previous_sampler=False):
         if not method:
             raise Exception("You must specify a do sampling method.")
-        if not self._obj._causal_model:
+        if not self._obj._causal_model or not use_previous_sampler:
             self._obj._causal_model = CausalModel(self._obj,
                                                   [xi for xi in x.keys()][0],
                                                   outcome,
@@ -41,7 +41,7 @@ class CausalAccessor(object):
                                                   proceed_when_unidentifiable=proceed_when_unidentifiable)
         self._obj._identified_estimand = self._obj._causal_model.identify_effect()
         do_sampler_class = do_samplers.get_class_object(method + "_sampler")
-        if not self._obj._sampler:
+        if not self._obj._sampler or not use_previous_sampler:
             self._obj._sampler = do_sampler_class(self._obj,
                                                   self._obj._identified_estimand,
                                                   self._obj._causal_model._treatment,

dowhy/do_samplers/mcmc_sampler.py

@@ -109,25 +109,16 @@ class McmcSampler(DoSampler):
 
     def do_sample(self, x):
         self.reset()
-        print(self._df.sample(10))
         g_for_surgery = nx.DiGraph(self.g)
         g_modified = self.do_x_surgery(g_for_surgery, x)
-        print(self._df.sample(10))
-
         self._df = self.make_intervention_effective(x)
-        print(self._df.sample(10))
-
         g_modified, trace = self.sample_prior_causal_model(g_modified,
                                                            self._df,
                                                            self._variable_types,
                                                            initialization_trace=self.fit_trace)
-        print(self._df.sample(10))
-
         for col in self._df:
             if col in trace and col not in self._treatment_names:
                 self._df[col] = trace[col]
-        print(self._df.sample(10))
-
         return self._df.copy()
 
     def _construct_sampler(self):