CIMA Lab

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(@)ncsu.edu or send an email to cima-lab-ncsu@googlegroups.com to subscribe.

CIMA news and activities (here is our website)

CIMA Group Meeting: 2025 Spring

CIMA Group Meeting: 2024 Fall

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

Resources

Information for CIMA students

Resources for writing and exams

Student Award

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

  1. NISS New Researcher Presentation Award from the NISS Virtual New Researchers Conference, Ke Zhu, 2025
  2. Best Presentation Award from the 38th New England Statistics Symposium, Yi Liu, 2025
  3. Best Student Poster Award from Workshop on Biostatistics & Bioinformatics, Yi Liu, 2025
  4. GSA Conferences and Travel Award, North Carolina State University, Shubhajit Sen, 2025
  5. Gertrude M. Cox Academic Achievement Award, North Carolina State University, Siqi Cao, 2025
  6. Merck Research Fellowship Award, North Carolina State University, Yi Liu, 2025
  7. DISS Best Poster Award from Duke Industry Statistics Symposium, Yi Liu, 2025
  8. IMS Hannan Graduate Student Travel Award from the Institute of Mathematical Statistics, Yi Liu, 2025
  9. GSA Conferences and Travel Award, North Carolina State University, Yi Liu, 2024
  10. Student Paper Competition Award from the LiDA Conference, Chenyin Gao, 2025
  11. Paige Plagge Award for good citizenship from Statistics North Carolina State University to a graduate student with an outstanding academic record and has enhanced the life of fellow students with encouragement, generosity and/or humor, Chenyin Gao, 2024
  12. ICSA Student Paper Award, ICSA Applied Statistics Symposium, Chenyin Gao, 2024
  13. ENAR RAB Student Poster Award Competition, 2nd place, ENAR Spring Meeting, Siyi Liu, 2024
  14. FDA-OCE-ASA Oncology Educational Fellowship, Taekwon Hong, 2023
  15. JSM Student Poster Award (honorable mention) from the ASA Biopharmaceutical Section in cooperation with the FDA Statistical Association, Sarah Fairfax, 2023
  16. BIOP RISW Student Travel Award from the ASA Biopharmaceutical Section in cooperation with the FDA Statistical Association, Siyi Liu, 2023
  17. JSM Student Paper Award from the ASA Section on Nonparametric Statistics, Joint Statistical Meetings, Yichi Zhang, 2023
  18. IBM Student Research Award, the 34th New England Statistics Symposium, Lili Wu, 2021
  19. DISS Best Poster Award from Duke Industry Statistics Symposium, Joe Zhao, 2021
  20. JSM Paper Award from the Section on Statistics in Epidemiology, Lili Wu, 2021
  21. ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Andrew Giffin, 2021
  22. ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Shuhan Tang, 2021
  23. ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Lin Dong, 2020

Current Members

Postdoc fellows

Ph.D. candidates

Alumni and Dissertation Involving Causal Inference Research

Previous postdoc fellows

Previous Ph.D. candidates

Ph.D. Advisor (2019–2024)
Thesis: Advanced statistical causal inference and individualized treatment regime learning: Data Integration, Inference, and Matching. [link]
Ph.D. Co-Advisor with Wenbin Lu (2019–2024)
Thesis: Advances in Policy Evaluation and Learning: Targeting, Truncation by Death, and One-sided Feedback. [link]
Ph.D. Advisor (2019–2024)
Thesis: Advanced Statistical Methods for Data Integration and Tensor Completion in Causal Inference. [link]
Ph.D. Advisor (2019–2024)
Thesis: Treatment effect evaluation in longitudinal studies with missing data and data integration. [link]
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]