Fan Li

My primary research interests lie in causal inference, health data science, and health service and policy. Causal inference concerns designing and evaluating treatments or interventions in randomized experiments and observational studies. It is central to decision making in many disciplines, including social sciences, medicine, and policy. It is known as comparative effectiveness research (in health studies), or program evaluation (in economics), or A/B testing (in online experiments). My research in causal inference, mostly motivated from real world problems in medicine, policy and economics, focuses on the following topics.

• Propensity score methods
Propensity score is arguably one of the most widely-used causal inference methods in observational studies. I have worked extensively on propensity score weighting methods. One of my main contributions is to develop the overlap weighting [Link] method (Li, Morgan, Zaslavsky, 2018), where, in binary treatments, each unit is weighted by its probability of being assigned to the opposite group. This method has many statistical and scientific advantages over the traditional inverse probability weighting method. We have also extended the method to multiple treatments and marginal structural models for time-varying treatments. I have also worked on propensity score weighting methods for multilevel or clustered data.

More information about the overlap weights is here.

• Clinical trial emulation using observational data
• Combining clinical trial and external control data
• Intercurrent events in clinical trials and principal stratification
• Natural- and quasi-experiments: instrumental variable, RDD and DiD
• Machine learning methods for causal inference
• Missing data

My research has been generously funded by NSF, NIH, FDA, and PCORI.