Causal inference at the intersection of machine learning and statistics: opportunities and challenges

Professor Jennifer Hill, NYU Steinhardt

There has been increasing interest in the past decade in use of machine learning tools in causal inference to help reduce reliance on parametric assumptions and allow for more accurate estimation of heterogeneous effects. This talk reviews the work in this area that capitalizes on Bayesian Additive Regression Trees, an algorithm that embeds a tree-based machine learning technique within a Bayesian framework to allow for flexible estimation and valid assessments of uncertainty. It will further describe extensions of the original work to address common issues in causal inference: lack of common support, violations of the ignorability assumption, and generalizability of results to broader populations. More general principles in the application of machine learning to causal inference will then be explored through a discussion of the results of a recent Causal Inference Data Analysis Challenge that helped to highlight the features of machine learning algorithms that are key to superior performance in observational causal inference settings.