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Likelihood estimate of treatment effects under selection bias
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-3183-3756
Department of Statistics, Pukyong National University, South Korea.
Department of Statistics, Seoul National University, South Korea.
2013 (engelsk)Inngår i: Statistics and its Interface, ISSN 1938-7989, E-ISSN 1938-7997, Vol. 6, nr 3, s. 349-359Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We consider methods for estimating the causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, a simple comparison of treated and control outcomes will not generally yield valid estimates of casual effect. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based on some strong assumptions, which are not directly testable. In this paper, we present an alternative modelling approach to draw causal inferences by using a shared random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but also is less sensitive to model misspecifications, which we consider, than existing methods.

sted, utgiver, år, opplag, sider
2013. Vol. 6, nr 3, s. 349-359
Emneord [en]
causal inference, likelihood, propensity score, random-effect model
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-12070DOI: 10.4310/SII.2013.v6.n3.a5ISI: 000325167700006OAI: oai:DiVA.org:du-12070DiVA, id: diva2:613387
Tilgjengelig fra: 2013-03-27 Laget: 2013-03-27 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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