[Theory] REMINDER: 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.

*Bio:* Yixin Wang is a PhD student in the Statistics Department of Columbia
University, advised by Professor David Blei. Her research interests lie in
Bayesian statistics, machine learning, and causal inference. Prior to
Columbia, she completed undergraduate studies in
mathematics and computer science at the Hong Kong University of Science and
Technology. Her research has received several awards, including the INFORMS
data mining best paper award, student paper awards from American
Statistical Association Biometrics Section and Bayesian Statistics Section,
and the ICSA conference young researcher award.


*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
<https://voices.uchicago.edu/machinelearning/events/>
Mary C. Marre
Faculty Administrative Support
*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>*


On Thu, Feb 6, 2020 at 2:41 PM Mary Marre <mmarre at ttic.edu> wrote:

> *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.
>
> *Bio:* Yixin Wang is a PhD student in the Statistics Department of
> Columbia University, advised by Professor David Blei. Her research
> interests lie in Bayesian statistics, machine learning, and causal
> inference. Prior to Columbia, she completed undergraduate studies in
> mathematics and computer science at the Hong Kong University of Science
> and Technology. Her research has received several awards, including the
> INFORMS data mining best paper award, student paper awards from American
> Statistical Association Biometrics Section and Bayesian Statistics Section,
> and the ICSA conference young researcher award.
>
>
> *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
> <https://voices.uchicago.edu/machinelearning/events/>
>
> Mary C. Marre
> Faculty Administrative Support
> *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>*
>
>
> On Sat, Feb 1, 2020 at 2:55 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *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
>> <https://voices.uchicago.edu/machinelearning/events/>Sign up for
>> announcement email list at
>> https://lists.uchicago.edu/web/subscribe/ml-announc
>> <https://lists.uchicago.edu/web/subscribe/ml-announce.>
>>
>>
>> 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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