*Presentation will be recorded.
Abstract:
There is a growing interest in individual-level causal questions to enable personalized decision-making. For example, what happens to a particular patient’s health if we prescribe a drug to them, or what happens to a particular consumer’s behavior if we recommend a product to them? Conducting large-scale randomized experiments to answer such questions is often impractical due to cost or ethical concerns. Observational data offer a valuable alternative, but their lack of explicit randomization makes statistical analysis particularly challenging.
In this talk, we exploit the richness of modern observational data to develop methods for personalized causal inference. In the first part, we introduce a new framework for causal inference using exponential family modeling. In particular, we reduce answering causal questions to learning exponential family from one sample. En route, we introduce a computationally tractable alternative to maximum likelihood estimation for learning exponential family. In the second part, we leverage ideas from doubly robust estimation to enable causal inference with black-box matrix completion, when parametric modeling is not feasible.
Bio:
Abhin Shah is a final-year Ph.D. student in Electrical Engineering and Computer Science at MIT, where he is fortunate to be advised by Prof. Devavrat Shah, Prof. Gregory Wornell, and Prof. Alberto Abadie. He is a recipient of MIT’s Jacobs Presidential Fellowship. He interned at Google Research in 2021 and at IBM Research in 2020. Prior to MIT, he graduated from IIT Bombay with a bachelor’s degree in Electrical Engineering. His research designs practically relevant and theoretically sound methods for causal inference using tools from statistics, machine learning, and econometrics.