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

Wed Jun 12 02:35 PM -- 02:40 PM (PDT) @ Room 103
We propose orthogonal random forest, an algorithm that incorporates double machine learning---a method of using Neyman-orthogonal moments to reduce sensitivity with respect to nuisance parameters to estimate the target parameter---with generalized random forests---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 assumption 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 data and real data, and show that it consistently outperforms baseline approaches.

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.