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…

Abstract: As ML models become increasingly powerful, it is an attractive proposition to use them in important decision making pipelines, in collaboration with human decision makers. But how should a human being and a machine learning model collaborate to reach decisions that are better than either of them could achieve on their own? If the human and the ML model were perfect Bayesians, operating in a setting with a commonly known and correctly specified prior, Aumann's classical agreement theorem would give us…