TheĀ  Statistics Lab for Causal Inference and Missing Data Analysis is led by Dr. Shu Yang. Many important questions in chronic diseases and cancer are about the effects of treatments, e.g., approving drugs, implementing health policies, or identifying optimal personalized treatment strategies. The answers to these questions often rely on complex real-world data suffering from confounding, non-compliance, drop-outs, missing values, etc.

Our research is to develop innovative statistical methods for making accurate inferences about treatment effects from complex observational and clinical studies, including marginal structural models, structural nested models, inverse probability weighting, and matching methods. This research falls into the general area of causal inference and missing data analyses. Our research team applies the novel methods in environmental health, cardiovascular diseases, HIV infection and cancer research to identify effective treatment strategies.

We are always looking for highly motivated students and postdocs to join CIMA, contact me at syang24(@) or send an email to to subscribe.

A postdoc position is open. Apply here.


2021 Fall CIMA Group Meeting


Group Photo

Our kick off meeting