Covariate Distribution Balance via Propensity Scores

36 Pages Posted: 18 Oct 2018 Last revised: 6 Apr 2020

See all articles by Pedro H. C. Sant'Anna

Pedro H. C. Sant'Anna

Vanderbilt University - College of Arts and Science - Department of Economics

Xiaojun Song

Peking University - Guanghua School of Management

Qi Xu

Vanderbilt University - College of Arts and Science - Department of Economics

Date Written: February 15, 2019

Abstract

This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed procedure attempts to estimate a propensity score model by making the underlying covariate distribution of different treatment groups as close to each other as possible. Our estimators are data-driven, do not rely on tuning parameters such as bandwidths, admit an asymptotic linear representation, and can be used to estimate different treatment effect parameters under different identifying assumptions, including unconfoundedness and local treatment effects. We derive the asymptotic properties of inverse probability weighted estimators for the average, distributional, and quantile treatment effects based on the proposed propensity score estimator and illustrate their finite sample performance via Monte Carlo simulations and two empirical applications.

Keywords: Causal inference; Empirical process; Inverse probability weighting; Minimum distance; Quantile treatment effects; Treatment effect heterogeneity

Suggested Citation

Sant'Anna, Pedro H. C. and Song, Xiaojun and Xu, Qi, Covariate Distribution Balance via Propensity Scores (February 15, 2019). Available at SSRN: https://ssrn.com/abstract=3258551 or http://dx.doi.org/10.2139/ssrn.3258551

Pedro H. C. Sant'Anna (Contact Author)

Vanderbilt University - College of Arts and Science - Department of Economics ( email )

Box 1819 Station B
Nashville, TN 37235
United States

HOME PAGE: http://https://sites.google.com/site/pedrohcsantanna/

Xiaojun Song

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Qi Xu

Vanderbilt University - College of Arts and Science - Department of Economics ( email )

Box 1819 Station B
Nashville, TN 37235
United States

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