Machine Learning and Causal Inference for Policy Evaluation

This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, and propose to divide the features of a model into causal features, whose values may be manipulated in a counterfactual policy environment, and attributes.