Online Interdisciplinary Seminars on Statistical Methodology for Social and Behavioral Research
The online interdisciplinary seminars on statistical methodology for social and behavioral research is supported by the department of statistics and the department of education psychology in the University of Connecticut (UCONN), the Statistical and Applied Mathematical Sciences Institute (SAMSI) and the New England Statistical Society (NESS). The seminar is held online via WebEx and anyone in the world can join and it is scheduled monthly on Friday noon. The aims of the seminar are to promote the connection between the statistics and social/behavioral science communities and to encourage more graduate students to participate in the interdisciplinary research.
INFORMATION ABOUT PAST SEMINARS IS AVAILABLE HERE.
For announcements and WebEx live streaming links, please contact Tracy Burke (firstname.lastname@example.org).
For questions related to the seminars, please feel free to contact organizers
(Prof. Xiaojing Wang (email@example.com) and Prof. Betsy McCoach (firstname.lastname@example.org) ).
Dr. Fan Li, Duke University
Friday, 10/01/2021, 12pm
Overlap Weighting for Causal Inference
Covariate balance is crucial for causal comparisons. Weighting is a common strategy to balance covariates in observational studies. We propose a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. Within the class, we highlight the overlap weighting method, which has been widely adopted in applied research. The overlap weight of each unit is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable exact balance property. Extension of overlap weighting to multiple treatments, survival outcomes, and subgroup analysis will also be discussed.
Dr. Fan Li is a Professor in the Department of Statistical Science, with a secondary appointment at the Department of Biostatistics and Bioinformatics, at Duke University. Her main research interest is causal inference – designs and analysis for evaluating treatments and interventions in randomized experiments and observational studies, and their applications to health studies (also known as comparative effectiveness research) and computational social science. Dr. Li also works on the interface between causal inference and machine learning. She has developed methods for propensity score, clinical trials, randomized experiments (e.g. A/B testing), difference-in-differences, regression discontinuity designs, representation learning, and Bayesian methods. She has also worked on statistical methods for missing data, as well as Bayesian graphical modeling for genomics and neuroimaging data.