Jing Qin, PhD
National Institute of Allergy and Infectious Diseases

A shape restricted propensity score matching method in casual inference
(joint work with Yukun Liu, East China Normal University)


Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM method based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed estimator is asymptotically semiparametric efficient for the univariate case, and achieves this level of efficiency in the multivariate case when the outcome and the propensity score depend on the covariate in the same direction.

We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.

Bio: Jing Qin is a Mathematical Statistician at Biostatistics Research Branch in National Institute of Allergy and Infectious Diseases. He earned his Ph.D. from University of Waterloo (1992) and then became an assistant Professor at University of Maryland, College Park. Before moving to National Institute of Health (2004), he worked at Memorial Sloan-Kettering Cancer Center for 5 years. Dr. Qin’s research interests include empirical likelihood method, case-control study, biased sampling problems (now called covariate shift or prior probability shift problems in machine learning literature), survival analysis, missing data, causal inference, genetic mixture models, generalized linear models, survey sampling and microarray data analysis. He was elected as an American Statistical Society Fellow, 2006. Dr. Qin published a monograph on "Biased sampling, over-identified parametric problems and beyond" in 2017 (Springer, ICSA book series in statistics).

DATE: Wednesday, 3/29/23
TIME: 4:00 PM
PLACE: Virtual
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m636152ee63a668032e47201fadcb0a90