Added blb inference option to the OrthoForest

* Added Bootstrap of Little Bags inference to the ORF classes
* Added tests and updated notebook
* Fixed the marginal effect shape when T is a vector
* Fixed bugs and reorganized class functionality

doc/spec/estimation/forest.rst

@@ -363,8 +363,7 @@ and the `ForestLearners Jupyter notebook <https://github.com/microsoft/EconML/bl
     >>> est.fit(Y, T, W, W)
     <econml.ortho_forest.ContinuousTreatmentOrthoForest object at 0x...>
     >>> print(est.effect(W[:2]))
-    [[1. ]
-     [1.2]]
+    [1.00...  1.19...]
 
 Similarly, we can call :class:`.DiscreteTreatmentOrthoForest`:
 
@@ -377,7 +376,7 @@ Similarly, we can call :class:`.DiscreteTreatmentOrthoForest`:
     >>> est.fit(Y, T, W, W)
     <econml.ortho_forest.DiscreteTreatmentOrthoForest object at 0x...>
     >>> print(est.effect(W[:2]))
-    [1.  1.2]
+    [1.01... 1.25...]
 
 Let's now look at a more involved example with a high-dimensional set of confounders :math:`W`
 and with more realistic noisy data. In this case we can just use the default parameters

doc/spec/inference.rst

@@ -136,6 +136,27 @@ and the :class:`.ForestDRLearner`. You can enable such intervals by setting ``in
 This inference is enabled by our implementation of the :class:`.SubsampledHonestForest` extension to the scikit-learn
 :class:`~sklearn.ensemble.RandomForestRegressor`.
 
+
+OrthoForest Bootstrap of Little Bags Inference
+==============================================
+
+For the Orthogonal Random Forest estimators (see :class:`.ContinuousTreatmentOrthoForest`, :class:`.DiscreteTreatmentOrthoForest`), 
+we provide confidence intervals built via the bootstrap-of-little-bags approach ([Athey2019]_). This technique is well suited for
+estimating the uncertainty of the honest causal forests underlying the OrthoForest estimators. You can enable such intervals by setting
+``inference='blb'``, e.g.:
+
+.. testcode::
+
+    from econml.ortho_forest import ContinuousTreatmentOrthoForest
+    from econml.sklearn_extensions.linear_model import WeightedLasso
+    est = ContinuousTreatmentOrthoForest(n_trees=10,
+                                         min_leaf_size=3,
+                                         model_T=WeightedLasso(alpha=0.01),
+                                         model_Y=WeightedLasso(alpha=0.01))
+    est.fit(y, t, X, W, inference='blb')
+    point = est.const_marginal_effect(X)
+    lb, ub = est.const_marginal_effect_interval(X, alpha=0.05)
+
 .. todo::    
     * Subsampling
     * Doubly Robust Gradient Inference