IV estimator (#201)

* Add 2SLS estimator with examples.
* Update scipy requirement per tensorflow.

causalml/inference/iv/__init__.py

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+from .iv_regression import IVRegressor

causalml/inference/iv/iv_regression.py

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+import numpy as np
+import pandas as pd
+
+from causalml.inference.meta.utils import convert_pd_to_np
+import statsmodels.api as sm
+from statsmodels.sandbox.regression.gmm import IV2SLS
+
+
+class IVRegressor(object):
+    ''' A wrapper class that uses IV2SLS from statsmodel
+
+    A linear 2SLS model that estimates the average treatment effect with endogenous treatment variable.
+    '''
+
+    def __init__(self):
+        '''
+        Initializes the class.
+        '''
+
+        self.method = '2SLS'
+
+    def fit(self, X, treatment, y, w):
+        ''' Fits the 2SLS model.
+
+        Args:
+            X (np.matrix or np.array or pd.Dataframe): a feature matrix
+            treatment (np.array or pd.Series): a treatment vector
+            y (np.array or pd.Series): an outcome vector
+            w (np.array or pd.Series): an instrument vector
+        '''
+
+        X, treatment, y, w = convert_pd_to_np(X, treatment, y, w)
+
+        exog = sm.add_constant(np.c_[X, treatment])
+        endog = y
+        instrument = sm.add_constant(np.c_[X, w])
+
+        self.iv_model = IV2SLS(endog=endog, exog=exog, instrument=instrument)
+        self.iv_fit = self.iv_model.fit()
+
+    def predict(self):
+        '''Returns the average treatment effect and its estimated standard error
+
+        Returns:
+            (float): average treatment effect
+            (float): standard error of the estimation
+        '''
+
+        return self.iv_fit.params[-1], self.iv_fit.bse[-1]