Maximum entropy in the mean methods in propensity score matching for interval and noisy data

Laura H. Gunn, Henryk Gzyl (), Enrique ter Horst, Miller Janny Ariza and German Molina

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 18, 4581-4597

Abstract: In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observational errors; or (iii) both as intervals and with bounded errors. We show that entropy in the mean methods for these three cases generally outperform benchmark error-free approaches.

Date: 2019
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DOI: 10.1080/03610926.2018.1497656

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