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Propensity Score Matching

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Advanced Statistics in Criminology and Criminal Justice

Abstract

This chapter examines a common method for creating matched comparison samples for assessing the impacts of treatments or interventions. The approach, termed propensity score matching (PSM), uses regression models to create estimates for the propensity of treated and untreated individuals to be selected into treatment. In this sense, it focuses its main interest on the mechanisms that underlie selection and its success in creating equivalent groups is based on the researcher’s ability to effectively model the selection mechanism. One advantage of PSM in evaluation studies is that it does not require that the researcher identify all variables that are meaningfully correlated with treatment and outcome, but rather that a valid estimate of propensity for treatment is gained. A second advantage of PSM is that it only creates matches for cases that have similar propensities, thus avoiding the empty cell problem common in regression analyses that seek to identify treatment outcomes. Another advantage of PSM is that the reporting of outcomes is usually straight forward and easily communicated. As in other regression approaches, the availability of a wide array of relevant covariates is essential for producing a valid set of outcome analyses.

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Notes

  1. 1.

    For discussions of these approaches, see Shadish et al. (2002).

  2. 2.

    Of course, there are other issues that might impact the validity of the treatment effect, such as measurement error.

  3. 3.

    For discussions of the limitations of the PSM approach, see Harrell and Slaughter (2020), and Loughran et al. (2015).

  4. 4.

    For details on the computation of MHbounds, see Becker and Caliendo (2007).

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Authors and Affiliations

  1. Institute of Criminology, Faculty of Law, Hebrew University of Jerusalem, Hebrew University of Jerusalem Inst. of Criminology, Fac. of Law, Inst. of Criminology, Fac. of Law, Fairfax, VA, USA, Jerusalem, Israel

    David Weisburd

  2. Department of Criminology, Law and Society, George Mason University, Manassas, VA, USA

    David B. Wilson

  3. Department of Criminal Justice, Temple University, Philadelphia, PA, USA

    Alese Wooditch

  4. Department of Sociology, Iowa State University Department of Sociology, Ames, IA, USA

    Chester Britt

Authors
  1. David Weisburd
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  2. David B. Wilson
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  3. Alese Wooditch
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  4. Chester Britt
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Correspondence to David Weisburd .

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Weisburd, D., Wilson, D.B., Wooditch, A., Britt, C. (2022). Propensity Score Matching. In: Advanced Statistics in Criminology and Criminal Justice. Springer, Cham. https://doi.org/10.1007/978-3-030-67738-1_10

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  • DOI: https://doi.org/10.1007/978-3-030-67738-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67737-4

  • Online ISBN: 978-3-030-67738-1

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