addeed policy learning to readme (#440)

* addeed policy learning to readme

README.md

@@ -502,6 +502,47 @@ as p-values and z-statistics. When the CATE model is linear and parametric, then
 
   </details>
 
+
+### Policy Learning
+
+You can also perform direct policy learning from observational data, using the doubly robust method for offline
+policy learning. These methods directly predict a recommended treatment, without internally fitting an explicit
+model of the conditional average treatment effect.
+
+<details>
+  <summary>Doubly Robust Policy Learning (click to expand)</summary>
+
+```Python
+from econml.policy import DRPolicyTree, DRPolicyForest
+from sklearn.ensemble import RandomForestRegressor
+
+# fit a single binary decision tree policy
+policy = DRPolicyTree(max_depth=1, min_impurity_decrease=0.01, honest=True)
+policy.fit(y, T, X=X, W=W)
+# predict the recommended treatment
+recommended_T = policy.predict(X)
+# plot the binary decision tree
+plt.figure(figsize=(10,5))
+policy.plot()
+# get feature importances
+importances = policy.feature_importances_
+
+# fit a binary decision forest
+policy = DRPolicyForest(max_depth=1, min_impurity_decrease=0.01, honest=True)
+policy.fit(y, T, X=X, W=W)
+# predict the recommended treatment
+recommended_T = policy.predict(X)
+# plot the first tree in the ensemble
+plt.figure(figsize=(10,5))
+policy.plot(0)
+# get feature importances
+importances = policy.feature_importances_
+```
+
+
+  ![image](images/policy_tree.png)
+</details>
+
 To see more complex examples, go to the [notebooks](https://github.com/Microsoft/EconML/tree/master/notebooks) section of the repository. For a more detailed description of the treatment effect estimation algorithms, see the EconML [documentation](https://econml.azurewebsites.net/).
 
 # For Developers
@@ -569,6 +610,10 @@ contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additio
 
 # References
 
+Athey, Susan, and Stefan Wager.
+**Policy learning with observational data.**
+Econometrica 89.1 (2021): 133-161.
+
 X Nie, S Wager.
 **Quasi-Oracle Estimation of Heterogeneous Treatment Effects.**
 [*Biometrika*](https://doi.org/10.1093/biomet/asaa076), 2020
@@ -606,3 +651,7 @@ S. Wager, S. Athey.
 Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. **Deep IV: A flexible approach for counterfactual prediction.** [*Proceedings of the 34th International Conference on Machine Learning, ICML'17*](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf), 2017.
 
 V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and a. W. Newey. **Double Machine Learning for Treatment and Causal Parameters.** [*ArXiv preprint arXiv:1608.00060*](https://arxiv.org/abs/1608.00060), 2016.
+
+Dudik, M., Erhan, D., Langford, J., & Li, L.
+**Doubly robust policy evaluation and optimization.**
+Statistical Science, 29(4), 485-511, 2014.

azure-pipelines.yml

@@ -36,6 +36,7 @@ jobs:
           switch -Wildcard ($file) {
             "README.md" { Continue }
             "prototypes/*" { Continue }
+            "images/*" { Continue }
             "doc/*" { $docChanges = $true; Continue }
             "notebooks/*" { $nbChanges = $true; Continue }
             default { $codeChanges = $true; Continue }