[Sds-seminars] talk today at 4pm @YINS (328 of 17 Hillhouse): Yixin Wang on "The Blessings of Multiple Causes"

[Dan Spielman]

YIXIN WANG, Columbia University
The Blessings of Multiple Causes
Monday, February 17, 20204:00PM to 5:00PM
YINS see map
<http://maps.google.com/?q=17+Hillhouse+Avenue%2C+Rm.+328%2C+New+Haven%2C+CT%2C+06511%2C+us>

17 Hillhouse Avenue, Rm. 328
New Haven, CT 06511
Website <http://www.stat.columbia.edu/~yixinwang/>
Information and 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.
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