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Alan M. Zaslavsky develops statistical methods for measuring health-care quality and disparities. His research includes missing data, hierarchical Bayesian models and Bayesian inference, bringing statistical adjustment to comparisons between health-care providers and populations.^[ref]

With Fan Li and Kari Lock Morgan, he developed a framework for balancing covariates through propensity score weighting. Their overlap weights emphasize people with a substantial chance of receiving either treatment, defining an overlap-weighted average treatment effect for that comparison population.^[ref]

Fan Li is the Duke Statistical Science researcher who coauthored Balancing Covariates via Propensity Score Weighting by Fan Li, Kari Lock Morgan and Alan M. Zaslavsky. Her author homepage and the paper's institutional byline identify this particular researcher.^[ref]

For propensity score $e(X)$, overlap weights assign weight $1-e(X)$ to a treated person and $e(X)$ to an untreated person. These bounded weights emphasize shared treatment eligibility and target a population with density proportional to $e(X)(1-e(X))$. Causal interpretation still requires the relevant no unmeasured confounding assumption and positivity in causal inference.^[ref]

Kari Lock Morgan studies the design of randomized experiments, especially how to improve baseline covariate balance. Her work on restricted randomization specifies acceptable balance before an experiment and redraws allocations that fail that criterion. Inference must account for the resulting assignment procedure.^[ref]

With Fan Li and Alan M. Zaslavsky, she studied propensity score weighting for observational studies. For a logistic propensity score fitted by maximum likelihood estimation with an intercept, their overlap weights exactly balance weighted means of the included covariates under the stated fitting conditions.^[ref]