fixed bug in causal effects

setup.py

@@ -63,7 +63,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.0.1.7",
+    version="5.0.1.8",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",

tigramite/causal_effects.py

@@ -2367,6 +2367,9 @@ def fit_bootstrap_of(self, method, method_args,
             getattr(self, method)(**method_args_bootstrap)
             self.bootstrap_results[b] = deepcopy(self.model)
 
+        # Reset model
+        self.model = self.original_model
+
         return self
 
 
@@ -2552,44 +2555,42 @@ def lin_f(x): return x
     # print(causal_effects.get_optimal_set())
 
     # # Fit causal effect model from observational data
-    causal_effects.fit_wright_effect(
+    causal_effects.fit_total_effect(
         dataframe=dataframe, 
         # mask_type='y',
-        # estimator=LinearRegression(),
+        estimator=LinearRegression(),
         )
 
+
+    # # Fit causal effect model from observational data
+    causal_effects.fit_bootstrap_of(
+        method='fit_total_effect',
+        method_args={'dataframe':dataframe,  
+        # mask_type='y',
+        'estimator':LinearRegression()
+        },
+        seed=4
+        )
+
+
     # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
     dox_vals = np.array([1.]) #np.linspace(0., 1., 1)
     intervention_data = dox_vals.reshape(len(dox_vals), len(X))
-    pred_Y = causal_effects.predict_wright_effect( 
+    pred_Y = causal_effects.predict_total_effect( 
             intervention_data=intervention_data)
     print(pred_Y)
 
 
-    # # Fit causal effect model from observational data
-    causal_effects.fit_bootstrap_of(
-        method='fit_wright_effect',
-        method_args={'dataframe':dataframe,  
-        # mask_type='y',
-        # 'estimator':LinearRegression()
-        }
-        )
 
 
     # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
     dox_vals = np.array([1.]) #np.linspace(0., 1., 1)
     intervention_data = dox_vals.reshape(len(dox_vals), len(X))
     conf = causal_effects.predict_bootstrap_of(
-        method='predict_wright_effect',
+        method='predict_total_effect',
         method_args={'intervention_data':intervention_data})
     print(conf)
 
-    # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
-    dox_vals = np.array([1.]) #np.linspace(0., 1., 1)
-    intervention_data = dox_vals.reshape(len(dox_vals), len(X))
-    pred_Y = causal_effects.predict_wright_effect( 
-            intervention_data=intervention_data)
-    print(pred_Y)
 
 
     # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go