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Association: Name: American Sociological Association URL: http://www.asanet.org
Citation: URL: http://www.allacademic.com/meta/p20695_index.html Direct Link: HTML Code:

Li, Jui-Chung. "A Hazard-Model Approach to Propensity Score Estimation for Studying the Treatment Effect of an Event" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA, Aug 12, 2005 <Not Available>. 2008-10-23 <http://www.allacademic.com/meta/p20695_index.html>

Li, J. A. , 2005-08-12 "A Hazard-Model Approach to Propensity Score Estimation for Studying the Treatment Effect of an Event" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA Online <APPLICATION/PDF>. 2008-10-23 from http://www.allacademic.com/meta/p20695_index.html

Publication Type: Conference Paper/Unpublished Manuscript Abstract: The propensity score method has increasingly become a popular strategy for studying the treatment effects in sociology. Developed in the context of policy/program evaluation, the methodological literature has typically assumed that whether an individual receives the treatment is endogenous but when an individual receives the treatment is exogenous. This assumption, despite plausible when the treatment of interest is a policy/program, is unlikely to hold when the treatment is an event. However, studying the effect of an event is a much more common research problem than studying the effect of a policy/program in sociological applications. In this paper, I discuss methodological issues regarding the conventional logit approach to propensity score estimation when applied to studying the effect of an event. Taking up on these issues, I propose a general hazard-model approach to propensity score estimation. This approach is preferable to the conventional approach in that it not only accommodates a plausible dynamic behavioral model for the treatment assignment mechanism of an event, but allows the investigator to include time-varying covariates as well. Empirical applications are illustrated by studying the average treatment effect of divorce on data from the National Longitudinal Survey of Youth 1979.

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