B0287
Title: Covariate distribution balance via propensity scores
Authors: Pedro SantAnna - Vanderbilt University (United States) [presenting]
Xiaojun Song - Guanghua School of Management, Peking University (China)
Qi Xu - Vanderbilt University (United States)
Abstract: The propensity score plays an important role in causal inference with observational data. However, it is well documented that under slight model misspecifications, propensity score estimates based on maximum likelihood can lead to unreliable treatment effect estimators. To address this practical limitation, a new framework is proposed for estimating propensity scores that mimics randomize control trials (RCT) in settings where only observational data is available. More specifically, given that in RCTs the joint distribution of covariates are balanced between treated and not-treated groups, we propose to estimate the propensity score by maximizing the covariate distribution balance. The proposed propensity score estimators, which we call the integrated propensity score (IPS), are data-driven, do not rely on tuning parameters such as bandwidths, admit an asymptotic linear representation, and can be used to estimate many different treatment effect measures in a unified manner. We derive the asymptotic properties of inverse probability weighted estimators for the average, distributional and quantile treatment effects based on the IPS and illustrate their relative performance via Monte Carlo simulations and three empirical applications. An implementation of the proposed methods is provided in the new package \texttt{IPS} for \texttt{R}.
