Overview
Multivariate regression-based methods dominate the research literature in criminology for estimating the effects of causal variables. As many scholars have noted, however, regression suffers from a number of methodological and practical limitations (Berk 2004). On its own, regression provides a poor analytic framework for conceptualizing causal inference, offers limited tools to address nonlinear relationships, and encourages researchers to cherry-pick models in a manner that may bias the results of a study. Despite these issues, criminologists have failed to take full advantage of other approaches to observational research developed in the fields of statistics and biostatistics.
One alternative is propensity score matching (Rosenbaum and Rubin 1983), an analytic method closely associated with the Rubin Causal Model (RCM; Rubin 2008; Holland 1986). Though a number of criminological studies have used propensity score approaches (e.g., Haviland et al. 2007; MacDonald et al. 2007...
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Acknowledgements
The data used in this case study were originally collected as part of a grant funded by the National Institute on Drug Abuse (NIDA) (grant R01DA16722) to Andrew Morral at the RAND Corporation. The opinions expressed in this entry are those of the authors and do not represent the official positions of NIDA or the RAND Corporation.
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Grunwald, B., MacDonald, J.M. (2014). Propensity Score Matching. In: Bruinsma, G., Weisburd, D. (eds) Encyclopedia of Criminology and Criminal Justice. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5690-2_46
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