{"title":"形状限制条件下用于因果推断的无调整参数倾向得分匹配方法","authors":"Yukun Liu , Jing Qin","doi":"10.1016/j.jeconom.2024.105829","DOIUrl":null,"url":null,"abstract":"<div><p>Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM approach to causal inference based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed causal effect estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate through the same index <span><math><mrow><msup><mrow><mi>X</mi></mrow><mrow><mo>⊤</mo></mrow></msup><mi>β</mi></mrow></math></span>. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105829"},"PeriodicalIF":9.9000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tuning-parameter-free propensity score matching approach for causal inference under shape restriction\",\"authors\":\"Yukun Liu , Jing Qin\",\"doi\":\"10.1016/j.jeconom.2024.105829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM approach to causal inference based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed causal effect estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate through the same index <span><math><mrow><msup><mrow><mi>X</mi></mrow><mrow><mo>⊤</mo></mrow></msup><mi>β</mi></mrow></math></span>. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.</p></div>\",\"PeriodicalId\":15629,\"journal\":{\"name\":\"Journal of Econometrics\",\"volume\":\"244 1\",\"pages\":\"Article 105829\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Econometrics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S030440762400174X\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Econometrics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030440762400174X","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Tuning-parameter-free propensity score matching approach for causal inference under shape restriction
Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM approach to causal inference based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed causal effect estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate through the same index . We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.
期刊介绍:
The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.