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Double/Debiased Machine Learning for Treatment and Structural Parameters

---- Acknowledgments ----

We would like to acknowledge research support from the National Science Foundation. We also thank participants of the MIT Stochastics and Statistics seminar, the Kansas Econometrics conference, the Royal Economic Society Annual Conference, The Hannan Lecture at the Australasian Econometric Society meeting, The Econometric Theory lecture at the EC2 meetings 2016 in Toulouse, The CORE 50th Anniversary Conference, The Becker-Friedman Institute Conference on Machine Learning and Economics, The INET conferences at USC on Big Data, the World Congress of Probability and Statistics 2016, the Joint Statistical Meetings 2016, the New England Day of Statistics Conference, CEMMAP's Masterclass on Causal Machine Learning, and St. Gallen's summer school on “Big Data", for many useful comments and questions. We would like to thank Susan Athey, Peter Aronow, Jin Hahn, Guido Imbens, Mark van der Laan, and Matt Taddy for constructive comments. We thank Peter Aronow for pointing us to the literature on targeted learning on which, along with prior works of Neyman, Bickel, and the many other contributions to semiparametric learning theory, we build. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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