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…

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…