BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250712T182049EDT-4247P9epmw@132.216.98.100 DTSTAMP:20250712T222049Z DESCRIPTION:Machine Learning for Causal Inference\n\nParticiper à la réunio n en ligne : Zoom\n \n ID de réunion : 965 2536 7383\n \n Code secret : 42 1254\n \n Colloque organisé conjointement avec Statlab (CRM)\n \n Résumé: Give n advances in machine learning over the past decades\, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible. In this talk\, I’ll discuss how machine learning tools can be rigorously integrated into observational study analy ses\, and how they interact with classical statistical ideas around random ization\, semiparametric modeling\, double robustness\, etc. I’ll also sur vey some recent advances in methods for treatment heterogeneity. When depl oyed carefully\, machine learning enables us to develop causal estimators that reflect an observational study design more closely than basic linear regression based methods.\n \n Biographie: Stefan Wager is an assistant prof essor of Operations\, Information\, and Technology at Stanford University. He completed a PhD in statistics at the same university in 2016 with Brad Efron and Guenther Walther\, and spent a year as a postdoctoral researche r at Columbia University. His research focuses on adapting ideas from mach ine learning to statistical problems that arise in scientific applications . His research interests are broad anc include causal inference\, non-para metric statistics\, uses of subsampling for data analysis\, and empirical Bayes methods.\n\n \n DTSTART:20200911T200000Z DTEND:20200911T210000Z SUMMARY:Stefan Wager ( Stanford University) URL:/mathstat/channels/event/stefan-wager-stanford-uni versity-324493 END:VEVENT END:VCALENDAR