Vira Semenova | Machine Learning for Causal Inference

On August 24-25, 2020 the CMSA hosted our sixth annual Conference on Big Data. The Conference featured many speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics.

Speaker: Vira Semenova

Title: Machine Learning for Causal Inference

Abstract:We study the problem of estimating average welfare in a dynamic discrete choice problem. We first show that value function is orthogonal to the conditional choice probability. Second, we give a correction term for the transition density of the state variable. The resulting orthogonal moment is robust to misspecification of the transition density and does not require this nuisance function to be consistently estimated. Third, we generalize this result by considering the weighted expected value. In this case, the orthogonal moment is doubly robust in the transition density and additional second-stage nuisance functions entering the correction term. We complete the asymptotic theory by providing bounds on second-order asymptotic terms. Joint work with Victor Chernozhukov and Whitney Newey.