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DTSTART:20180325T010000
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DTSTART:20181028T010000
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DTSTART;TZID=Europe/Paris:20181008T140000
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SUMMARY:Stefan WAGER (Université de Stanford) - "Machine Learning for Causal Inference"
DESCRIPTION:\nThe Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 8th of October 2018\nPlace: Room 1003. exceptionally room 1003\nStefan WAGER (Université de Stanford) – “Machine Learning for Causal Inference“ \nAbstract: 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. \nOrganizers:\nCristina BUTUCEA\, Alexandre TSYBAKOV\, Julie JOSSE\, Eric MOULINES\, Mathieu ROSENBAUM\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/jamal-najim-cnrs-upem-tba-2-2-3-5-2-2-2-2-3/
CATEGORIES:Statistics
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