References ========== .. [Chernozhukov2016] V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and a. W. Newey. Double Machine Learning for Treatment and Causal Parameters. *ArXiv e-prints*, July 2016. .. [Chernozhukov2017] V. Chernozhukov, M. Goldman, V. Semenova, and M. Taddy. Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels. *ArXiv e-prints*, December 2017. .. [Chernozhukov2018] V. Chernozhukov, D. Nekipelov, V. Semenova, and V. Syrgkanis. Two-Stage Estimation with a High-Dimensional Second Stage. 2018. .. [Hartford2017] 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*, 2017. .. [Jaggi2010] Martin Jaggi and Marek Sulovský. A simple algorithm for nuclear norm regularized problems. *Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel*, pages 471--478, 2010. .. [Kunzel2017] Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. Meta-learners for estimating heterogeneous treatment effects using machine learning. *arXiv preprint arXiv:1706.03461*, 2017. URL http://arxiv.org/abs/1706.03461. .. [Mackey2017] Lester W. Mackey, Vasilis Syrgkanis, and Ilias Zadik. Orthogonal machine learning: Power and limitations. *CoRR*, abs/1711.00342, 2017. URL http://arxiv.org/abs/1711.00342. .. [Newey2003] W. K. Newey and J. L. Powell. Instrumental variable estimation of nonparametric models. *Econometrica*, 71 (5): 1565--1578, 2003. .. [Foster2019] D. Foster and V. Syrgkanis. Orthogonal Statistical Learning. *arXiv preprint arXiv:1901.09036*, 2019. URL http://arxiv.org/abs/1901.09036. .. [Wager2018] S. Wager and S. Athey. Estimation and inference of heterogeneous treatment effects using random forests. *Journal of the American Statistical Association*, 113(523), pp.1228-1242, 2018. .. [Athey2019] S. Athey, J. Tibshirani and S. Wager. Generalized Random Forests. *Annals of Statistics*, 2019 .. [Oprescu2019] M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. *Proceedings of the 36th International Conference on Machine Learning*, 2019. URL http://proceedings.mlr.press/v97/oprescu19a.html. .. [Nie2017] X. Nie and S. Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. *arXiv preprint arXiv:1712.04912*, 2017. URL http://arxiv.org/abs/1712.04912. .. [Buhlmann2011] P. Bühlmann and S. van de Geer Statistics for High-Dimensional Data Springer Series in Statistics, 2011 URL https://www.springer.com/gp/book/9783642201912 .. [Robins1994] Robins, J.M., Rotnitzky, A., and Zhao, L.P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89,846–866. .. [Bang] Bang, H. and Robins, J.M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61,962–972. .. [Tsiatis] Tsiatis AA (2006). Semiparametric Theory and Missing Data. New York: Springer; 2006. .. [Dudik2014] Dudík, M., Erhan, D., Langford, J., & Li, L. (2014). Doubly robust policy evaluation and optimization. Statistical Science, 29(4), 485-511. .. [Athey2017] Athey, S., & Wager, S. (2017). Efficient policy learning. arXiv preprint arXiv:1702.02896. .. [Friedberg2018] Friedberg, R., Tibshirani, J., Athey, S., & Wager, S. (2018). Local linear forests. arXiv preprint arXiv:1807.11408. .. [Lundberg2017] Lundberg, S., Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. URL https://arxiv.org/abs/1705.07874 .. [Lewis2021] Lewis, G., Syrgkanis, V. (2021). Double/Debiased Machine Learning for Dynamic Treatment Effects. URL https://arxiv.org/abs/2002.07285 .. [Hernan2010] Hernán, Miguel A., and James M. Robins (2010). Causal inference. URL https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ .. [Syrgkanis2019] Syrgkanis, V., Lei, V., Oprescu, M., Hei, M., Battocchi, K., Lewis, G. (2019) Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments URL https://arxiv.org/abs/1905.10176