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Maximum entropy in the mean methods in propensity score matching for interval and noisy data |
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| Authors: | Laura H. Gunn Henryk Gzyl Enrique ter Horst Miller Janny Ariza |
| Affiliation: | 1. Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA;2. School of Public Health, Faculty of Medicine, Imperial College London, London, UK;3. Centro de Finanzas, IESA, Caracas, Venezuela;4. University of the Andes School of Management (Uniandes), Bogotá, Colombia;5. Ingenieria Financiera, Universidad Piloto de Colombia, Bogotá, Colombia |
| Abstract: | AbstractIn 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. |
| Keywords: | Propensity score matching observational studies maximum entropy in the mean data with bounded errors interval data |