Maximum entropy in the mean methods in propensity score matching for interval and noisy data. Issue 18 (17th September 2019)

• Record Type: Journal Article
• Authors: Gunn, Laura H.; Gzyl, Henryk; ter Horst, Enrique; Ariza, Miller Janny; Molina, German
• Is Part Of: Communications in statistics. Volume 48:Issue 18(2019)
• Page Start: 4581
• Page End: 4597
• Publication Date: 2019-09-17
• Subjects: Propensity score matching -- observational studies -- maximum entropy in the mean -- data with bounded errors -- interval data; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1497656
• Languages: English
• ISSNs: 0361-0926

Abstract

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.