Vasilis/docs

README.md

@@ -161,7 +161,7 @@ To install from source, see [For Developers](#for-developers) section below.
   <summary>Orthogonal Random Forests (click to expand)</summary>
 
   ```Python
-  from econml.ortho_forest import DMLOrthoForest, DROrthoForest
+  from econml.orf import DMLOrthoForest, DROrthoForest
   from econml.sklearn_extensions.linear_model import WeightedLasso, WeightedLassoCV
   # Use defaults
   est = DMLOrthoForest()
@@ -232,7 +232,7 @@ To install from source, see [For Developers](#for-developers) section below.
 * Linear final stage
 
 ```Python
-from econml.drlearner import LinearDRLearner
+from econml.dr import LinearDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = LinearDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -245,7 +245,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Sparse linear final stage
 
 ```Python
-from econml.drlearner import SparseLinearDRLearner
+from econml.dr import SparseLinearDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = SparseLinearDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -258,7 +258,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Nonparametric final stage
 
 ```Python
-from econml.drlearner import ForestDRLearner
+from econml.dr import ForestDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = ForestDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -275,7 +275,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Intent to Treat Doubly Robust Learner (discrete instrument, discrete treatment)
 
 ```Python
-from econml.ortho_iv import LinearIntentToTreatDRIV
+from econml.iv.dr import LinearIntentToTreatDRIV
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 from sklearn.linear_model import LinearRegression
 
@@ -294,7 +294,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05) # OLS confidence intervals
 
 ```Python
 import keras
-from econml.deepiv import DeepIVEstimator
+from econml.iv.nnet import DeepIV
 
 treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),
                                     keras.layers.Dropout(0.17),
@@ -309,11 +309,11 @@ response_model = keras.Sequential([keras.layers.Dense(128, activation='relu', in
                                   keras.layers.Dense(32, activation='relu'),
                                   keras.layers.Dropout(0.17),
                                   keras.layers.Dense(1)])
-est = DeepIVEstimator(n_components=10, # Number of gaussians in the mixture density networks)
-                      m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])), # Treatment model
-                      h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # Response model
-                      n_samples=1 # Number of samples used to estimate the response
-                      )
+est = DeepIV(n_components=10, # Number of gaussians in the mixture density networks)
+             m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])), # Treatment model
+             h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # Response model
+             n_samples=1 # Number of samples used to estimate the response
+             )
 est.fit(Y, T, X=X, Z=Z) # Z -> instrumental variables
 treatment_effects = est.effect(X_test)
 ```