Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and...

Machine learning is a growing method for causal inference. In machine learning settings, prediction errors are a commonly overlooked problem that can bias results and lead to arbitrarily incorrect parameter estimates. We consider a two-stage model where (1) machine learning is used to predict...

Slope coefficients in rank-rank regressions are popular measures of intergenerational mobility, for instance in regressions of a child's income rank on their parent's income rank. In this paper, we first point out that commonly used variance estimators such as the homoskedastic or robust...

This paper considers a multi-dimensional panel data model with multilevel factors when the numbers of cross-sections and time observations are large. We develop a multilevel iterative principal component (MIPC) method for estimation by iteratively updating between the slope coefficients and...

We extend extreme value statistics to independent data with possibly very different distributions. In particular, we present novel asymptotic normality results for the Hill estimator, which now estimates the extreme value index of the average distribution. Due to the heterogeneity, the...

This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a very general and more realistic setup in which the comparison graph consists of hyper-edges of possible heterogeneous sizes and...

I provide formulas for the influence function of two common misallocation statistics: TFP loss from misallocation and potential TFP gain from reallocation. I also show how to use a finite-difference approximation to Gateaux derivatives to obtain sample analogs to influence functions for...

We describe a framework for empirical welfare analysis that uses the causal estimates of a policy’s impact on net government spending. This framework provides guidance for which causal effects are (and are not) needed for empirical welfare analysis of public policies.The key ingredient is the...

This paper introduces a new bootstrap approach to the construction of confidence regions for Average Treatment Effect (ATE) identified sets. Minimum Hausdorff distance bootstrap confidence regions are developed and shown to be valid under suitable regularity. A novel measure of the discrepancy...