Fundamental statistical limits in causal inference

Abstract: Despite tremendous methodological advances in causal inference, there remain significant gaps in our understanding of the fundamental statistical limits of estimating various causal estimands from observational data. In this talk I will survey some recent work that aims to make some progress towards closing these gaps. Particularly, I will discuss the fundamental limits of estimating various important causal estimands under classical smoothness assumptions, under new “structure-agnostic” assumptions, in a discrete setup, and under partial smoothness assumptions. Via these fundamental limits we will also attempt to understand the optimality/sub-optimality of simple, practical procedures for estimating causal effects from observational data.