[Theory] 2/7 Machine Learning Seminar Series: Yixin Wang, Columbia University

[Mary Marre]

*When:*     Friday, February 7th at 10:30am



*Where:*    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *      Yixin Wang, Columbia University



*Title:*       The Blessings of Multiple Causes

*Abstract: *Causal inference from observational data is a vital problem,
but it comes with strong assumptions. Most methods assume that we observe
all confounders, variables that affect both the causal variables and the
outcome variables. But whether we have observed all confounders is a
famously untestable assumption. We describe the deconfounder, a way to do
causal inference from observational data allowing for unobserved
confounding. How does the deconfounder work? The deconfounder is designed
for problems of multiple causal inferences: scientific studies that involve
many causes whose effects are simultaneously of interest. The deconfounder
uses the correlation among causes as evidence for unobserved confounders,
combining unsupervised machine learning and predictive model checking to
perform causal inference. We study the theoretical requirements for the
deconfounder to provide unbiased causal estimates, along with its
limitations and tradeoffs. We demonstrate the deconfounder on real-world
data and simulation studies.

*Host:* Karen Livescu <klivescu at ttic.edu>


*University of Chicago  <https://www.uchicago.edu/>and** Toyota
Technological Institute at Chicago <http://www.ttic.edu/>*
Machine Learning Seminar Series
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Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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