Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
arXiv 9 Jun 2018 · Machine Learning · 31 citations (OpenAlex)
arXiv:1806.03467 · PDF · DOI · OpenAlex · Extracted main text
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 $\ell_1$-penalized regression achieves the required rate. We 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. We also provide a comprehensive empirical evaluation of our algorithm on both synthetic and real data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, S., Tibshirani, J., and Wager, S (2017) Generalized Random Forests | 1.000 | 10 | 4 | 100% |
| 2 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2017) Double/debiased/neyman machine learning of treatment effects | 0.928 | 4 | 4 | 100% |
| 3 | Wager, S. and Athey, S (2015) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.874 | 9 | 3 | 67% |
| 4 | Chernozhukov, V., Goldman, M., Semenova, V., and Taddy, M (2017) Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels | 0.874 | 5 | 2 | 100% |
| 5 | Tibshirani, J., Athey, S., Wager, S., Friedberg, R., Miner, L., and… (2018) grf: Generalized Random Forests (Beta), 2018 | 0.811 | 4 | 2 | 100% |
| 6 | Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and… Locally Robust Semiparametric Estimation | 0.585 | 3 | 1 | 100% |
| 7 | Hoeffding, W (1963) Probability inequalities for sums of bounded random variables | 0.511 | 2 | 2 | 50% |
| 8 | Chernozhukov, V., Nekipelov, D., Semenova, V., and Syrgkanis, V (1806) Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models self | 0.511 | 2 | 1 | 100% |
| 9 | Nie, X. and Wager, S (2017) Learning Objectives for Treatment Effect Estimation | 0.511 | 2 | 1 | 100% |
| 10 | Robinson, P. M (1988) Root-n-consistent semiparametric regression | 0.511 | 2 | 1 | 100% |
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