Maximum entropy in the mean methods in propensity score matching for interval and noisy data. Issue 18 (17th September 2019) - Gunn, Laura H.; Gzyl, Henryk; ter Horst, Enrique; Ariza, Miller Janny; Molina, German
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.