Causal Inference with Selectively Deconfounded Data Joint Talk with ORIE

Abstract: One fundamental question in causal inference is to estimate the average treatment effect (ATE). In general, ATE is not identifiable when treatments and effects are observed, but confounders are not. Thus, to estimate the ATE, a practitioner must then either (a) collect deconfounded data; (b) run a clinical trial; or (c) elucidate further properties of the causal graph that might render the ATE identifiable. In this paper, we consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the ATE. Our theoretical results show that the inclusion of confounded data can significantly reduce the quantity of deconfounded data required to estimate the ATE to within a desired accuracy level. Moreover, in some cases---say, genetics---we could imagine retrospectively selecting samples to deconfound. We demonstrate that by actively selecting these samples based upon the (already observed) treatment and outcome, we can reduce our data dependence further. Our theoretical results establish that the worst-case relative performance of our approach (vs. random selection) is bounded while our best-case gains are unbounded. We perform extensive synthetic experiments to validate our theoretical results. Finally, we demonstrate the practical benefits of selective deconfounding using a large real-world dataset related to genetic mutation in cancer.

Bio: Kyra is a fifth-year Ph.D. candidate in Operations Research at Tepper School of Business, Carnegie Mellon University. Her advisors are Professor Sridhar Tayur and Professor Andrew Li. I Kyera also works closely with Professor Zachary Lipton and Professor Alan Scheller-Wolf, and is a part of the ACMI lab. Prior to joining CMU, Kyra received her BA degrees in Mathematics (with the Ann Kirsten Pokora Prize) and Economics from Smith College in May 2017. She completed her first year of college at UCSD in June 2014. In general, she is interested in solving real-world medical problems and specifically interested in efficient algorithms in precision medicine. Kyra's work lies in the intersection of optimization, machine learning, and medicine.