Webinar Series: Mathematical Foundations of Data Science

Synthetic Interventions

Abstract

As we face the resurgence of COVID-19 pandemic in the United States, a pressing question is facing us: shall we shut down the economy again? or can we tame the pandemic with effective policies while letting the economy remain (partially) open? Towards that, we first need to understand if shutting down the economy helped in the first place. And if it did, is it possible to achieve similar gains while partially opening up the economy? And if so, how does that translate into public health policy? 

To address such `what if scenario analysis’ questions, we propose a method of Synthetic Interventions (SI). It enables counterfactual estimates for all interventions of interest using observed data only. SI generalizes the classical Synthetic Controls (SC) method for causal inference using observational studies — it has similar data requirements as SC, but enables counterfactual estimates with multiple interventions, rather than single intervention as in SC. The SI method comes with a data-driven test to evaluate its applicability. In addition to explaining its utility in answering above mentioned questions, time permitting, we shall discuss applications in the context of policy design in the developing countries, online A/B testing and drug discovery.   

Based on joint works with Anish Agarwal, Abdullah Aalomar, Romain Cosson, Arnab Sarkar, Dennis Shen and Cindy Yang (all at MIT). Associated pre-prints: https://arxiv.org/abs/2006.07691 and https://arxiv.org/abs/2005.00072

Bio

Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at MIT. He is the founding director of Statistics and Data Science Center at MIT. He is a member of the Institute for Data, Systems and Society, LIDS, CSAIL and ORC at MIT. 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. He has received Erlang Prize from INFORMS Applied Probability Society and Rising Star Award from ACM Sigmetrics. He is a distinguished young alumni of his alma mater IIT Bombay. In 2013, he founded the machine learning start-up Celect, Inc. which helps retailers with optimizing inventory by accurate demand forecasting. In August 2019, Celect, Inc. was acquired by Nike, Inc.