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On Rosenbaum's Rank-based Matching Estimator

Matias D. Cattaneo, Fang Han, Zhexiao Lin

arXiv 12 Dec 2023 · Mathematics — Statistics Theory · publishedBiometrika (2024) · 1 citations (OpenAlex)

arXiv:2312.07683 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that Rosenbaum's rank-based matching estimator, when coupled with a regression adjustment, enjoys the properties of double robustness and semiparametric efficiency without the need to enforce restrictive covariate moment assumptions. Our theoretical findings further emphasize the statistical virtues of employing ranks for estimation and inference, more broadly aligning with the insights put forth by Peter Bickel in his 2004 Rietz lecture (Bickel, 2004).

Citation extraction

23
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Rosenbaum, P. R (2020) Design of Observational Studies1.00063100%
2Belloni, A., Chernozhukov, V., Chetverikov, D., and Kato, K (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results0.92843100%
3Cattaneo, M. D. and Farrell, M. H (2013) Optimal convergence rates, bahadur representation, and asymptotic normality of partitioning estimators self0.92843100%
4Cattaneo, M. D., Farrell, M. H., and Feng, Y (2020) Large sample properties of partitioning-based series estimators self0.92843100%
5Lin, Z., Ding, P., and Han, F (2023) Estimation based on nearest neighbor matching: from density ratio to average treatment effect self0.8746467%
6Lin, Z. and Han, F (2022) On regression-adjusted imputation estimators of the average treatment effect self0.8434475%
7Huang, J (2003) Local asymptotics for polynomial spline regression0.84333100%
8Newey, W. K (1997) Convergence rates and asymptotic normality for series estimators0.84333100%
9Cattaneo, M. D., Crump, R. K., Farrell, M. H., and Feng, Y (2023) On binscatter self0.73732100%
10Abadie, A. and Imbens, G. W (2006) Large sample properties of matching estimators for average treatment effects0.64422100%

Showing the top 10 of 23 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators0.638276
2Variance reduction combining pre-experiment and in-experiment data0.40511
3On the limiting variance of matching estimators0.40511
4Limit theorems of Azadkia-Chatterjee's conditional graph correlation0.40511
5An Introduction to Permutation Processes (version 0.5)0.00011