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Likelihood estimate of treatment effects under selection bias
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
Department of Statistics, Pukyong National University, South Korea.
Department of Statistics, Seoul National University, South Korea.
2013 (English)In: Statistics and its Interface, ISSN 1938-7989, E-ISSN 1938-7997, Vol. 6, no 3, p. 349-359Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2013. Vol. 6, no 3, p. 349-359
Keywords [en]
causal inference, likelihood, propensity score, random-effect model
National Category
Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
URN: urn:nbn:se:du-12070DOI: 10.4310/SII.2013.v6.n3.a5ISI: 000325167700006OAI: oai:DiVA.org:du-12070DiVA, id: diva2:613387
Available from: 2013-03-27 Created: 2013-03-27 Last updated: 2017-12-06Bibliographically approved

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  • apa
  • ieee
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  • chicago-author-date
  • chicago-note-bibliography
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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