Why propensity scores should be used for matching

Ben Jann

German Stata Users' Group Meetings 2017 from Stata Users Group

Abstract: In their paper titled Why Propensity Scores Should Not Be Used for Matching, Gary King and Richard Nielsen suggest that propensity-score matching (PSM) is inferior to other matching procedures such as Mahalanobis matching (King and Nielsen 2016). They argue that PSM approximates complete randomization, whereas other techniques approximate fully blocked randomization, and that fully blocked randomization dominates complete randomization in terms of statistical efficiency. They illustrate their argument using constructed examples, simulations, and applications to real data. Overall, their results suggest that PSM has dramatic deficiencies and should best be discarded. Although the claim about the superior efficiency of a fully blocked design over complete randomization is true (given a specific sample size), the problems King and Nielsen identify apply only under certain conditions. First, the complete randomization argument is valid only with respect to covariates that are not related to the treatment. Second, and more importantly, King and Nielsen's "PSM paradox" occurs only for specific variants of PSM. I will explain why this is the case, and I will show that other variants of PSM compare favorably with blocking procedures such as Mahalanobis matching. I will illustrate my arguments using a new matching software called "kmatch".

Date: 2017-09-20
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (10)

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