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Stefan WAGER (Université de Stanford) – "Machine Learning for Causal Inference"
Time: 2:00 pm – 3:15 pm
Date: 8th of October 2018
Place: Room 1003. exceptionally room 1003
Stefan WAGER (Université de Stanford) – “Machine Learning for Causal Inference“
Abstract: Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this talk, I will discuss general principles for estimating heterogeneous treatment effects in observational studies via loss minimization, and then present a random forest algorithm that builds on these principles. As established both formally and empirically, the proposed approach is an order of magnitude more robust to confounding that direct regression-based baselines.
Cristina BUTUCEA, Alexandre TSYBAKOV, Julie JOSSE, Eric MOULINES, Mathieu ROSENBAUM