[Sds-seminars] Fwd: [Statseminars] S&DS Talk, Speaker: Yixin Wang, 02/17, "The Blessings of Multiple Causes", DL220

[Dan Spielman]

---------- Forwarded message ---------
From: <elizavette.torres at yale.edu>
Date: Wed, Feb 12, 2020 at 1:17 PM
Subject: [Statseminars] S&DS Talk, Speaker: Yixin Wang, 02/17, "The
Blessings of Multiple Causes", DL220
To: <Statseminars at mailman.yale.edu>, <sds-majors at mailman.yale.edu>


[image: Department of Statistics and Data Science]
<https://statistics.yale.edu/>*   Monday, February 17, 2020 – Statistics
and Data Science Seminar  *

Featured Speaker: Yixin Wang, Columbia University

DATE: Monday, February 17, 2020

TIME: 4:00PM to 5:00PM

LOCATION: Dunham Lab.

10 Hillhouse Avenue, Rm. 220
<https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g>

New Haven
<https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g>
, CT
<https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g>
 06511
<https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g>

https://statistics.yale.edu/seminars/yixin-wang



*The Blessings of Multiple Causes*



*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.



3:45 p.m.   Pre-talk tea Dunham Lab,
<https://www.google.com/maps/search/10+Hillhouse+Avenue,+Rm.+220+New+Haven+,+CT+06511?entry=gmail&source=g>
Suite 222, Breakroom 228

For more details and upcoming events visit our website at
http://statistics.yale.edu/ .








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