Earlier versions of this paper were presented at the 2006 Annual Meeting of the Robert Wood Johnson Health & Society Scholars Program and the 2005 Winter Conference of the American Sociological Association Methodology Section. We thank Ross Stolzenberg for serving as the editor for this manuscript. We also thank David Harding and two anonymous reviewers for helpful comments and suggestions. Brand received support from the Robert Wood Johnson Foundation Health & Society Scholars at the University of Michigan and the Carolina Population Center NICHD training grant at the University of North Carolina–Chapel Hill. This research uses data from the Wisconsin Longitudinal Study (WLS) of the University of Wisconsin–Madison. Since 1991, the WLS has been supported principally by the National Institute on Aging (AG-9775 and AG-21079), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundation, and the Graduate School of the University of Wisconsin–Madison. A public use file of data from the Wisconsin Longitudinal Study is available from the Data and Program Library Service, University of Wisconsin–Madison, 1180 Observatory Drive, Madison, Wisconsin 53706 and at http://dpls.dacc.wisc.edu/WLS/wlsarch.htm. The ideas expressed herein are those of the authors. Direct all correspondence to Jennie E. Brand, University of North Carolina–Chapel Hill, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27514; email: email@example.com.
IDENTIFICATION AND ESTIMATION OF CAUSAL EFFECTS WITH TIME-VARYING TREATMENTS AND TIME-VARYING OUTCOMES*
Article first published online: 25 JUN 2007
Volume 37, Issue 1, pages 393–434, December 2007
How to Cite
Brand, J. E. and Xie, Y. (2007), IDENTIFICATION AND ESTIMATION OF CAUSAL EFFECTS WITH TIME-VARYING TREATMENTS AND TIME-VARYING OUTCOMES*. Sociological Methodology, 37: 393–434. doi: 10.1111/j.1467-9531.2007.00185.x
- Issue published online: 25 JUN 2007
- Article first published online: 25 JUN 2007
We develop an approach to identifying and estimating causal effects in longitudinal settings with time–varying treatments and time–varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.