Likelihood estimate of treatment effects under selection bias


Autoria(s): Alam, Moudud; Noh, Maengseok; Lee, Youngjo
Data(s)

2012

Resumo

We consider methods for estimating 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, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share 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 it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Formato

application/pdf

Identificador

http://urn.kb.se/resolve?urn=urn:nbn:se:du-11318

Idioma(s)

eng

Publicador

Högskolan Dalarna, Statistik

Department of Statistics, Pukyong National Univeristy

Department of Statistics, Seoul National Univeristy

Borlänge : Högskolan Dalarna

Relação

Working papers in transport, tourism, information technology and microdata analysis, 1650-5581 ; 2012:06

Direitos

info:eu-repo/semantics/openAccess

Palavras-Chave #Causal inference #h-likelihood #shared random-effect model #summer job effect
Tipo

Report

info:eu-repo/semantics/report

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