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Matching in R

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Design of Observational Studies

Part of the book series: Springer Series in Statistics ((SSS))

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Abstract

Simple calculations in the statistical language R illustrate the computations involved in one simple form of multivariate matching. The focus is on how matching is done, not on the many aspects of the design of an observational study. The process is made tangible by describing it in detail, step-by-step, closely inspecting intermediate results; however, essentially, three steps are illustrated: (1) creating a distance matrix, (2) adding a propensity score caliper to the distance matrix, and (3) finding an optimal match. In practice, matching involves bookkeeping and efficient use of computer memory that are best handled by dedicated software for matching. Sect. 14.10 describes currently available software.

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Notes

  1. 1.

    Dynarski [15, Table 2] presents two analyses, one with no covariates, the other with many more covariates, obtaining similar estimates of effect from both analyses. That is a reasonable approach in the context of her paper. In constructing a matched control group in this chapter, I have omitted a few of the covariates that Dynarski used, including “single-parent household” and “father attended college,” from a comparison involving a group defined by a deceased father. In general, if one wants to present parallel analyses with and without adjustment for a particular covariate, say “single-parent household,” then one should not match on that covariate, but should control for it in one of the parallel analyses using analytical techniques; e.g., Sect. 22.2 or [16, 32, 37, 42] and [36, §3.6] .

  2. 2.

    In detail, the covariates used in the match are (1) faminc: family income in units of $10,000; (2) incmiss: income missing (incmiss=1 if family income is missing, incmiss=0 otherwise); (3) black (black=1 if black, black=0 otherwise), (4) hispanic (hispanic=1 if hispanic, hispanic=0 otherwise), (5) afqtpct: Armed Forces Qualifications Test (AFQT), (6) edmissm: mother’s education missing (edmissm=1 if missing, edmissm=0 otherwise), (vii) edm: mother’s education (edm=1 for less than high school, edm=2 for high school, edm=3 for some college, edm=4 for BA degree or more), (viii) female (female=1 for female, female =0 for male).

  3. 3.

    Because of this restriction, the counts of seniors in various groups are slightly different than in [15].

  4. 4.

    Technically, the fitted probabilities in logit regression are invariant under affine transformations of the predictors.

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Authors and Affiliations

  1. Statistics Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Paul R. Rosenbaum

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R. Rosenbaum, P. (2020). Matching in R . In: Design of Observational Studies. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-46405-9_14

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