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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\u2010driven 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 an empirical application.<\/jats:p>","DOI":"10.1002\/jae.2909","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T05:58:22Z","timestamp":1654667902000},"page":"1093-1120","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Covariate distribution balance via propensity scores"],"prefix":"10.1002","volume":"37","author":[{"given":"Pedro H. 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