UBC Statistics Department Colloquium: Nonparametrics in causal inference: densities, heterogeneity, & beyond

Much work in causal inference focuses on finite-dimensional targets like average treatment effects. However, many substantively important causal questions involve inherently infinite-dimensional objects, such as counterfactual outcome distributions, heterogeneous treatment effect surfaces, and continuous treatment curves. These targets occupy a hybrid space between classical parameter estimation and nonparametric function estimation. In this talk, I survey some recent work involving these infinite-dimensional causal estimands, highlighting both model-based and model-free nonparametric approaches. I discuss how, despite the impossibility of root-n-rate estimation, ideas from semiparametric theory (like double robustness) continue to play a central role. Throughout I emphasize the relevance of these methods in applications in social sciences and medicine.

This talk is part of the UBC Statistics Colloquium Series, which features broad and accessible seminars throughout the term and is sponsored in part by the Constance van Eeden Endowment.