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Why propensity scores should be used for matching

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  • Ben Jann

    (University of Bern)

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".

Suggested Citation

  • Ben Jann, 2017. "Why propensity scores should be used for matching," German Stata Users' Group Meetings 2017 01, Stata Users Group.
    • Handle: RePEc:boc:dsug17:01
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    File URL: http://repec.org/dsug2017/Germany17_Jann.pdf
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    Cited by:

    1. Philipp vom Berge & Achim Schmillen, 2023. "Effects of mass layoffs on local employment—evidence from geo-referenced data," Journal of International Economic Law, Oxford University Press, vol. 23(3), pages 509-539.
    2. Philipp vom Berge & Achim Schmillen, 2023. "Effects of mass layoffs on local employment—evidence from geo-referenced data," Journal of Economic Geography, Oxford University Press, vol. 23(3), pages 509-539.
    3. Bornmann, Lutz & Tekles, Alexander, 2021. "Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts," Journal of Informetrics, Elsevier, vol. 15(3).
    4. Alda, Erik, 2020. "The effects of body-worn cameras on police efficiency: A study of local police agencies in the US," MPRA Paper 103887, University Library of Munich, Germany.
    5. Gabriele Beccari & Francesco Marchionne & Beniamino Pisicoli, 2022. "Alternative financing and investment in intangibles: evidence from Italian firms," Mo.Fi.R. Working Papers 174, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    6. Chih‐Hai Yang & Chia‐Hui Huang & Wei‐Hsuan Chang, 2021. "Does Reduction In The Tax Credit Rate Retard R&D Activity? Evidence From Taiwan'S R&D Tax Credit Reform In 2010," Contemporary Economic Policy, Western Economic Association International, vol. 39(2), pages 398-415, April.
    7. Alda, Erik, 2020. "The effects of body-worn cameras on police efficiency: A study of local police agencies in the US," MPRA Paper 103994, University Library of Munich, Germany.

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