October 2, 2019

12:00 pm / 1:00 pm

Venue

Hackerman B-17

Abstract:
Causal inference from observational data is a vitalproblem, but it comes with strong assumptions. Most methods require thatwe 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 with weaker assumptions than the classical methods require.
How does the deconfounderwork? While traditional causal methods measure the effect of a single cause on an outcome, many modern scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. The deconfounderuses the correlation among multiple causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference.We demonstrate the deconfounderon real-world data and simulation studies, and describe the theoretical requirements for the deconfounderto provide unbiased causal estimates.
 
Bio:
David Bleiis a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. He studies probabilistic machine learning, including its theory, algorithms, and application. David has receivedseveral awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), BlavatnikFaculty Award (2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship (2017), and a Simons Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research.He is a fellow of the ACM and the IMS.
[*] https://arxiv.org/abs/1805.06826
 
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