Publications/Accepted Papers:

  • Deep Partial Least Squares for Instrumental Variable Regression (with Nicholas Polson and Vadim Sokolov) [arXiv] – Forthcoming in Applied Stochastic Models in Business and Industry

Working Papers:

  • Job Market Paper: Adaptive Estimation of Partially Identified Treatment Effects [Draft] [Slides]

Abstract: This paper proposes a non-parametric algorithm for estimating and inferring partially identified treatment effects. In particular, we introduce multivariate random forests that can be used to fit the bounds of treatment effects identified as the solution to a set of local moment equations. To detect heterogeneous subgroups, multivariate random forests adaptively search for subsets of data that exhibit the highest variation in the treatment effect bounds. We provide consistency guarantees for the estimators of the treatment effect bounds and derive their asymptotic normality under certain regularity conditions and sample splitting assumptions. Simulation experiments and applications to the National Longitudinal Survey of Youth reveal significant heterogeneity in the effect of the Head Start program on years of schooling.

Work in Progress: