Laboratory for Computational Sensing and Robotics Seminar: David Blei

Description

Dr. David Blei, a professor of Statistics and Computer Science at Columbia University and a member of the Columbia Data Science Institute, will give a talk entitled "The Blessings of Multiple Causes [*]" for the Laboratory for Computational Sensing and Robotics.

Abstract:

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 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 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.