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Poster
Orthogonal Random Forest for Causal Inference
Miruna Oprescu · Vasilis Syrgkanis · Steven Wu

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #195
in Posters Wed »

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.

Keywords: Causality · Information Theory and Estimation · Non-parametric Methods · Statistical Learning Theory

Author Information

Miruna Oprescu (Microsoft Research)

Miruna Oprescu is a Data and Applied Scientist at Microsoft Research New England. In her current role, Miruna works alongside researchers and software engineers to build the next generation machine learning tools for interdisciplinary applications. Miruna spends her time between two projects: project ALICE, a Microsoft Research initiative aimed at applying artificial intelligence concepts to economic decision making, and the Machine Learning for Cancer Immunotherapies initiative, a collaboration with doctors and cancer researchers with the goal of applying machine learning techniques to improving cancer therapies.

Vasilis Syrgkanis (Microsoft Research)
Steven Wu (University of Minnesota)

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