Special Seminar in Computing and Mathematical Sciences

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.