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Orthogonal Random Forests for Causal Inference Steven Wu - PowerPoint PPT Presentation

Apr 03, 2024 •268 likes •341 views

Orthogonal Random Forests for Causal Inference Steven Wu University of Minnesota Joint work with: Miruna Oprescu and Vasilis Syrgkanis Microsoft ResearchNew England Motivating examples Dynamic pricing Clinical trials Targeted advertising

  1. Orthogonal Random Forests for Causal Inference Steven Wu University of Minnesota Joint work with: Miruna Oprescu and Vasilis Syrgkanis Microsoft Research—New England

  2. Motivating examples Dynamic pricing Clinical trials Targeted advertising • Conditional average treatment estimation (CATE) from observational data • Outcome ! " (demand) • Treatment # " (pricing) • Feature $ " that captures heterogeneity (income level) • Confounders % " (other observed variables) Treatment effect Our Goal: CATE estimation ! = ' $, % ⋅ # + + , $, % + - 0 , 1 = 2 ' , $, % $ = 1] # = . , $, % + /

  3. More generally… • In the language of econometrics: Given a target feature ! , find a solution " # (!) to & '((; ", ℎ # (,, -)) , = !] = 0 with score function ' and nuisance function ℎ # • Other examples: non-parametric regression, instrumental variable regression, local maximum likelihood estimation, etc.

  4. Orthogonal Random Forest (ORF) Generalized Random Forest (GRF) Orthogonality (or double ML) [Wager & Athey 2018; Athey et al. 2019] [Neyman1979; Chernozhukov et al. 2017] Method: Method: • Perform two-stage estimation: first • Non-parametric random forest-based estimate nuisance, then estimate target ! " estimation Pros: Pros: • Robust to high-dimensional confounders • Allows more general functions ! " Cons: Cons: • Assumes parametric form ! " • Does not directly handle high-dimensional nuisance functions

  5. Main theoretical results for ORF Accuracy for ORF estimate ! " • Consistency error rate • Asymptotic normality Nuisance estimation procedure • Forest Lasso method that leverages locally sparse structure

  6. Empirical Evalua,on

  7. Orthogonal Random Forests for Causal Inference Poster: Wed Jun 12th @ Pacific Ballroom #195

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