Online Causal Inference Seminar
A regular international causal inference seminar.
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Upcoming Seminar Presentations
All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 5:30 pm Berlin / 12:30 pm Beijing.
Tuesday, December 6, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)
- Speaker: Jose Zubizarreta (Harvard University)
- Title: Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference
- Discussant: Mike Baiocchi (Stanford University)
- Abstract: A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment are used in pursuit of this principle. Basic methods are matching, regression, and weighting. In this talk, we will examine the connections between these methods through their underlying mathematical programs. We will study their strengths and weaknesses in terms of study design, computational tractability, and statistical efficiency. Overall, we will discuss the role of mathematical optimization for the design and analysis of studies of causal effects.
Join us in our special Causality & Fairness seminar series in January, 2023!
Format and Rules
The seminars are held on Zoom and last 60 minutes. Our seminars will typically follow one of three formats:
Format 1: single presentation
45 minutes of presentation
10 minutes of discussion, led by an invited discussant
Q&A, time permitting
Format 2: two presentations
Two presentations, 25-30 minutes each
Q&A, time permitting
Format 3: interview
40-45 minute conversation with leader in causal inference
15-20 minutes of Q&A
A moderator collects audience questions in Q&A section.
Moderators may ask you to unmute yourself to participate in the discussion. Please note that you may be recorded if you activate your audio or video during the seminar.
Michael Celentano (Stanford), Guido Imbens (Stanford), Ying Jin (Stanford), Georgia Papadogeorgou (University of Florida), Ema Perkovic (University of Washington), Dominik Rothenhäusler (Stanford), Qingyuan Zhao (University of Cambridge)
Susan Athey (Stanford), Guillaume Basse (Stanford), Peter Bühlmann (ETH Zürich), Peng Ding (Berkeley), Andrew Gelman (Columbia), Guido Imbens (Stanford), Fabrizia Mealli (Florence), Nicolai Meinshausen (ETH Zürich), Maya Petersen (Berkeley), Thomas Richardson (UW), Dominik Rothenhäusler (Stanford), Jas Sekhon (Berkeley/Yale), Stefan Wager (Stanford)
Feedback and Suggestions
We gratefully acknowledge support by the Stanford Department of Statistics and the Stanford Data Science Initiative.