A prototypical example of causal inference with observational data pertains evaluating impact of a policy, such as universal background check for gun purchase, on outcome of interest, such as gun violence, with respect to alternatives such as no background check or stricter form of gun control law. Unlike setting of clinical trials where randomized control experiments are feasible, such is not feasibility for policy evaluation. To address this, we present a causal framework, synthetic interventions (SI), that extends synthetic control (SC) to the multiple treatment setting. Formally, given N units (e.g. states) and D interventions (e.g. various gun control policies), the aim of SI is to estimate the counterfactual outcome of each unit under each of the D interventions (including control). We showcase the efficacy of the SI framework on several real-world applications, such as running data-efficient A/B tests in e-commerce and correcting for bias in clinical trials due to dropouts. Finally, we show how to produce tight confidence intervals around our causal estimates. The key to our framework is the connection between causal inference and tensor estimation.

Based on joint work with Anish Agarwal and Dennis Shen, both at MIT.

Bio: Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at MIT. He was the founding director of Statistics and Data Science Center (SDSC) from 2016-2020 at MIT. He is a member of the Institute for Data, Systems and Society, LIDS, SDSC, CSAIL and ORC. His current research interests include algorithms for machine learning, causal inference and social data processing. He has received paper awards from INFORMS Applied Probability Society, NeurIPS, ACM Sigmetrics and IEEE Infocom and Test of time paper awards (2019-2020) from ACM Sigmetrics. He has received Erlang Prize from INFORMS Applied Probability Society and Rising Star Award from ACM Sigmetrics. He is a distinguished alumni of his alma mater IIT Bombay. In 2013, he founded the machine learning start-up Celect (part of Nike since 2019) which helps retailers with optimizing inventory by accurate demand forecasting.