Department of Statistics pres.
Statistics Department Seminar Series: David Blei, Professor, Department of Statistics and Computer Science, Columbia University
"The Blessings of Multiple Causes [*]"
How does the deconfounder work? 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 deconfounder uses 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 deconfounder on real-world data and simulation studies, and describe the theoretical requirements for the deconfounder to provide unbiased causal estimates.
This is joint work with Yixin Wang. [*] https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1686987
Biography: David Blei is 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 received several 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), Blavatnik Faculty 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.
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