Comments on ‘A weighting analogue to pair matching in propensity score analysis’ by L. Li and T. Greene

Orihara Shunichiro (), Kawamura Taishi and Taguri Masataka
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Orihara Shunichiro: Graduate School of Data Science, Yokohama City University, Yokohama, Kanagawa, Japan
Kawamura Taishi: Graduate School of Data Science, Yokohama City University, Yokohama, Kanagawa, Japan
Taguri Masataka: Graduate School of Data Science, Yokohama City University, Yokohama, Kanagawa, Japan

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 53-60

Abstract: Li and Greene (A weighting analogue to pair matching in propensity score analysis. Int J Biostat 2013;9:215–34) propose that estimates derived by the matching weight (MW) estimator are similar to those derived by the one-to-one propensity score matching estimator. The MW estimator has some useful properties, however, some regularity conditions need to be confirmed to derive an asymptotic distribution since the MW has a non-differentiable point. In this letter, we confirm the asymptotic distribution of the MW estimator and the sufficient conditions to achieve it.

Keywords: asymptotic normality; causal inference; matching weight (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2021-0090

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