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Activity Number: 65 - Causal Inference with Latent Variables
Type: Invited
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Mental Health Statistics Section
Abstract #316833
Title: The Deconfounder: What Is It? What Is Its Theory? Is it Useful?
Author(s): David Blei* and Yixin Wang
Companies: Columbia University and U.C. Berkeley

I will discuss the deconfounder algorithm and the assumptions it requires. Several refinements have been suggested around the theory of the deconfounder. Among these, Imai and Jiang clarified the assumption of "no unobserved single-cause confounders." Using their assumption, I will clarify the theory. Finally, I will discuss whether the deconfounder is useful in practice. This talk will largely follow Wang and Blei (2020) [*].


Authors who are presenting talks have a * after their name.

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