Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables andthe outcome variables. But whether we have observed all confounders isa famously untestable assumption. We describe the deconfounder, a wayto do causal inference with weaker assumptions than the classical methods require.
How does the deconfounder work? While traditional causal methodsmeasure the effect of a single cause on an outcome, many modern scientific studies involve multiple causes, different variables whoseeffects are simultaneously of interest. The deconfounder uses thecorrelation among multiple causes as evidence for unobservedconfounders, combining unsupervised machine learning and predictivemodel checking to perform causal inference. We demonstrate thedeconfounder on real-world data and simulation studies, and describethe theoretical requirements for the deconfounder to provide unbiasedcausal estimates.
This is joint work with Yixin Wang.
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.
Reception for attendees at 3:30 in CS 150