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Propensity score matching for multiple treatment levels: A CODA-based contribution

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  • Hajime Seya
  • Takahiro Yoshida

Abstract

This study proposes a simple technique for propensity score matching for multiple treatment levels under the strong unconfoundedness assumption with the help of the Aitchison distance proposed in the field of compositional data analysis (CODA).

Suggested Citation

  • Hajime Seya & Takahiro Yoshida, 2017. "Propensity score matching for multiple treatment levels: A CODA-based contribution," Papers 1710.08558, arXiv.org.
    • Handle: RePEc:arx:papers:1710.08558
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    References listed on IDEAS

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    1. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    2. Shu Yang & Guido W. Imbens & Zhanglin Cui & Douglas E. Faries & Zbigniew Kadziola, 2016. "Propensity score matching and subclassification in observational studies with multi‐level treatments," Biometrics, The International Biometric Society, vol. 72(4), pages 1055-1065, December.
    3. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    4. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
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    Cited by:

    1. Takahiro Yoshida & Rim Er-rbib & Morito Tsutsumi, 2019. "Which Country Epitomizes the World? A Study from the Perspective of Demographic Composition," Sustainability, MDPI, vol. 11(22), pages 1-16, November.

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