The Deconfounder: What Is It? What Is Its Theory? Is it Useful?

• 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
• Author(s): David Blei* and Yixin Wang
• Companies: Columbia University and U.C. Berkeley

Abstract

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) [*].

[*] https://arxiv.org/abs/2003.04948