The  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 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 to join CIMA, contact me at syang24(@) or send an email to to subscribe.

CIMA news and activities (here is our website)

CIMA Group Meeting: 2024 Spring

CIMA Group Meeting: 2023 Fall

CIMA Group Meeting: 2023 Spring

CIMA Group Meeting: 2022 Fall

CIMA Group Meeting: 2022 Spring

CIMA Group Meeting: 2021 Fall


Information for CIMA students

Resources for writing and exams

Student travel or paper award opportunities (managed by Yi Liu)


Postdoc fellows

Ph.D. candidates

Alumni and Dissertation involving Causal Inference Research

Ph.D. Co-Advisor with Wenbin Lu (2019–2024)

Thesis: Analysis of Irregularly Spaced Longitudinal Market Transaction Data. [link]

Ph.D. Committee Member (2018–2023)

Thesis: Advances in causal inference and the study of interlocus gene conversion [link]

Ph.D. Co-Advisor with Minh Tang (2018–2023)

Thesis: Statistical inference with randomized SVD for signal-plus-noise matrix models and causal inference with continuous interventions [link]

Ph.D. Advisor (2018–2023)

Thesis: Doubly robust estimators of causal effects in observational studies: theory and practice [link]

Ph.D. Co-Advisor with Emily Hector (2018–2023)

Thesis: Advances in Matching Methods for Causal Inference with Multiple Treatments [link]

Ph.D. Advisor (2016–2022)

Thesis: Robust Causal Inference Methods for Using Randomized Clinical Trial and Observational Study [link]

Ph.D. Committee Member (2017–2022)

Thesis: Covariance Function Estimation and Causal Inference Methods [link]

Ph.D. Co-Advisor with Brian Reich (2017–2022)

Thesis: Advances in Semiparametric Quantile Regression [link]

Ph.D. Co-Advisor with Sujit Ghosh (2017–2022)

Thesis: Semiparametric Inference of Randomized Controlled Trials and Observational Studies [link]

Ph.D. Advisor (2016–2021)

Thesis: Spatially Varying and Multi-Source Data Integrative Causal Inference [link]

Ph.D. Committee Member (2017–2021)

Thesis: Advanced Methods in Bayesian Variable Selection and Causal Inference [link]

Ph.D. Committee Member (2017–2021)

Thesis: Online Testing and Semiparametric Estimation of Complex Treatment Effect [link]

Ph.D. Co-Advisor with Marie Davidian (2014–2021)

Thesis: Conducting Causal Inference on Partially Observed Data via Imputation and Matching [link]

Ph.D. Co-Advisor with Brian Reich (2016–2020)

Thesis: Methods for Causal Inference on Spatial Data with Environmental and Public Health Applications [link]

Ph.D. Co-Advisor with Eric Laber (2015–2019)

Thesis: Semiparametric Methods for Decision Making and Causal Effect Generalization [link]

Ph.D. Committee Member (2015–2019)

Thesis: Bayesian Methods for Optimal Treatment Allocation and Causal Inference [link]