The Statistics Lab for Causal Inference and Missing Data Analysis is led by Dr. Shu Yang. Many important questions in public health and medicine are about the effects of treatments, e.g., drug and vaccine approval, health policy implementation, and identifying optimal personalized treatment strategies. The answers to these questions often rely on complex, single- or multi-source real-world data suffering from confounding, non-compliance, drop-outs, missing values, etc.

Our research focuses on developing innovative statistical methods for accurate inference of treatment effects in observational and clinical studies. This includes advancements in marginal structural models, structural nested models, inverse probability weighting, matching, multiply robust and other related approaches. Our research falls into the general area of causal inference and missing data analysis. We apply these methods to real-world problems in environmental health, cardiovascular diseases, HIV infection and cancer research to identify effective treatment strategies.

We are always actively looking for highly motivated students and postdocs to join CIMA. To stay updated or express interest, please subscribe by emailing: cima-lab-ncsu@googlegroups.com.