ﻻ يوجد ملخص باللغة العربية
Inverse Probability Weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of small denominators, trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this paper, we first show that the IPW estimator can have different (Gaussian or non-Gaussian) asymptotic distributions, depending on how close to zero the probability weights are and on how large the trimming threshold is. As a remedy, we propose an inference procedure that is robust not only to small probability weights entering the IPW estimator but also to a wide range of trimming threshold choices, by adapting to these different asymptotic distributions. This robustness is achieved by employing resampling techniques and by correcting a non-negligible trimming bias. We also propose an easy-to-implement method for choosing the trimming threshold by minimizing an empirical analogue of the asymptotic mean squared error. In addition, we show that our inference procedure remains valid with the use of a data-driven trimming threshold. We illustrate our method by revisiting a dataset from the National Supported Work program.
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the textit{covariate shift}. To correct for sampling biases, most approaches, including the popular kernel mean matching (K
This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions. The method is non-asymptotic in the sense that it provides a finite sample bound on the difference between the tr
This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model average (MMA) estimator of Hansen (2007), but, unlike MMA, simultaneou
We propose a generalization of the linear panel quantile regression model to accommodate both textit{sparse} and textit{dense} parts: sparse means while the number of covariates available is large, potentially only a much smaller number of them have
One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities on sensitive attributes such as age, gender, or race. This paper addresses the question