Abstract
We propose the orthogonal random forest, an algorithm that combines Neyman-orthogonality to reduce sensitivity with respect to estimation error of nuisance parameters with generalized random forests (Athey et al., 2017)-a flexible non-parametric method for statistical estimation of conditional moment models using random forests. We provide a consistency rate and establish asymptotic normality for our estimator. We show that under mild assumptions on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters. We show that when the nuisance functions have a locally sparse parametrization, then a local l2-penalized regression achieves the required rate. Wc apply our method to estimate heterogeneous treatment effects from observational data with discrete treatments or continuous treatments, and we show that, unlike prior work, our method provably allows to control for a high-dimensional set of variables under standard sparsity conditions. Wc also provide a comprehensive empirical evaluation of our algorithm on both synthetic and real data.
Original language | English (US) |
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Title of host publication | 36th International Conference on Machine Learning, ICML 2019 |
Publisher | International Machine Learning Society (IMLS) |
Pages | 8655-8696 |
Number of pages | 42 |
ISBN (Electronic) | 9781510886988 |
State | Published - 2019 |
Event | 36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States Duration: Jun 9 2019 → Jun 15 2019 |
Publication series
Name | 36th International Conference on Machine Learning, ICML 2019 |
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Volume | 2019-June |
Conference
Conference | 36th International Conference on Machine Learning, ICML 2019 |
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Country/Territory | United States |
City | Long Beach |
Period | 6/9/19 → 6/15/19 |
Bibliographical note
Publisher Copyright:© 2019 International Machine Learning Society (IMLS).