Implement DynamicDML

econml/_cate_estimator.py

@@ -563,7 +563,7 @@ def effect(self, X=None, *, T0, T1):
         """
         Calculate the heterogeneous treatment effect :math:`\\tau(X, T0, T1)`.
 
-        The effect is calculatred between the two treatment points
+        The effect is calculated between the two treatment points
         conditional on a vector of features on a set of m test samples :math:`\\{T0_i, T1_i, X_i\\}`.
         Since this class assumes a linear effect, only the difference between T0ᵢ and T1ᵢ
         matters for this computation.

econml/dml/__init__.py

@@ -33,10 +33,14 @@
     Orthogonal Statistical Learning.
     ACM Conference on Learning Theory. `<https://arxiv.org/abs/1901.09036>`_
 
+.. [dynamicdml] Greg Lewis and Vasilis Syrgkanis.
+    Double/Debiased Machine Learning for Dynamic Treatment Effects.
+    `<https://arxiv.org/abs/2002.07285>`_, 2021.
 """
 
 from .dml import (DML, LinearDML, SparseLinearDML,
                   KernelDML, NonParamDML, ForestDML)
+from .dynamic_dml import DynamicDML
 from .causal_forest import CausalForestDML
 
 __all__ = ["DML",
@@ -45,4 +49,5 @@
            "KernelDML",
            "NonParamDML",
            "ForestDML",
-           "CausalForestDML", ]
+           "CausalForestDML",
+           "DynamicDML"]