Please find information below on an upcoming webinar, sponsored by the Mental Health Statistics Section.
Title: Machine Learning for Causal Inference
Presenter: Dr. Stefan Wager, Stanford University
Date and Time: Wednesday, February 24, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section
Registration Deadline: Tuesday, February 23, at 12:00 p.m. Eastern time
Description: Given 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 analyses, and how they interact with classical statistical ideas around randomization, semiparametric modeling, double robustness, etc. I'll also survey some recent advances in methods for treatment heterogeneity. When deployed carefully, machine learning enables us to develop causal estimators that reflect an observational study design more closely than basic linear regression-based methods.
Presenter: Dr. Stefan Wager is an assistant professor of Operations, Information, and Technology at the Stanford Graduate School of Business, and an assistant professor of Statistics (by courtesy). His research lies at the intersection of causal inference, optimization, and statistical learning.
Registration:
ASA Members: $20
Student ASA Member: $15
Nonmembers: $35
Each registration is allowed one connection to the webinar. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).
Registration Link: https://www.amstat.org/ASA/Education/Web-Based-Lectures.aspx#MLCI
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Adam Ciarleglio
Assistant Professor
George Washington University
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