From d864de345ebc269ff2db419e60ac0d6181de5b17 Mon Sep 17 00:00:00 2001
From: Ingrid Spielman <ingrid.spielman@flocabulary.com>
Date: Sat, 5 Nov 2016 16:53:11 -0700
Subject: [PATCH] added empty class CausalImpact

---
 causality/estimation/parametric.py | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/causality/estimation/parametric.py b/causality/estimation/parametric.py
index 997e440..104a781 100644
--- a/causality/estimation/parametric.py
+++ b/causality/estimation/parametric.py
@@ -32,22 +32,22 @@ def average_treatment_effect(self, X, start='Start', end='End', assignment='Assi
         control_final = control['End']
         del test, control
 
-        df = pd.DataFrame({'y' : test_initial, 
-                   'assignment' : [1. for i in test_initial], 
+        df = pd.DataFrame({'y' : test_initial,
+                   'assignment' : [1. for i in test_initial],
                    't' :[0. for i in test_initial] })
-        df = df.append(pd.DataFrame({'y' : test_final, 
-                                     'assignment' : [1. for i in test_final], 
+        df = df.append(pd.DataFrame({'y' : test_final,
+                                     'assignment' : [1. for i in test_final],
                                      't' :[1. for i in test_final] }))
 
-        df = df.append(pd.DataFrame({'y' : control_initial, 
-                                     'assignment' : [0. for i in control_initial], 
+        df = df.append(pd.DataFrame({'y' : control_initial,
+                                     'assignment' : [0. for i in control_initial],
                                      't' :[0. for i in control_initial] }))
 
-        df = df.append(pd.DataFrame({'y' : control_final, 
-                                     'assignment' : [0. for i in control_final], 
+        df = df.append(pd.DataFrame({'y' : control_final,
+                                     'assignment' : [0. for i in control_final],
                                      't' :[1. for i in control_final] }))
         del test_initial, test_final, control_initial, control_final
-        df['did'] = df['t'] * df['assignment'] 
+        df['did'] = df['t'] * df['assignment']
         df['intercept'] = 1.
 
         model = self.model(df['y'], df[['t', 'assignment','did', 'intercept']])
@@ -55,7 +55,7 @@ def average_treatment_effect(self, X, start='Start', end='End', assignment='Assi
         conf_int = result.conf_int().ix['did']
         expected = result.params['did']
         return conf_int[0], expected, conf_int[1]
-        
+
     def test_parallel_trend(self, X, start='Start', end='End', assignment='Assignment'):
         """
         This will find the average treatment effect on
@@ -64,7 +64,7 @@ def test_parallel_trend(self, X, start='Start', end='End', assignment='Assignmen
         that the average treatment effect between the test
         and control groups when neither is treated is 0.
 
-        The format for this dataset is the same as that 
+        The format for this dataset is the same as that
         for the real estimation task, except that the start
         time is some time before the experiment is run, and
         the end time is the starting point for the experiment.
@@ -145,4 +145,9 @@ def estimate_ATC(self, X, assignment, outcome, confounder_types, n_neighbors=5):
     def estimate_ATE(self, X, assignment, outcome, confounder_types, n_neighbors=5):
         att = estimate_ATT(self, X, assignment, outcome, confounder_types, n_neighbors=n_neighbors)
         atc = estimate_ATC(self, X, assignment, outcome, confounder_types, n_neighbors=n_neighbors)
-        return (atc+att)/2. 
+        return (atc+att)/2.
+
+
+class CausalImpact(object):
+    def __init__(self):
+        pass