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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/).
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.
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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.
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Dudik, M., Erhan, D., Langford, J., & Li, L.
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**Doubly robust policy evaluation and optimization.**
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