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: 2026 Spring
CIMA Group Meeting: 2025 Spring
CIMA Group Meeting: 2024 Spring
CIMA Group Meeting: 2023 Spring
CIMA Group Meeting: 2022 Spring
Resources
Resources for writing and exams
—> Student Award
Student travel or paper award opportunities (managed by Yi Liu)
- Honorable Mention of Student Paper Competition, the ASA Biopharmaceutical Section, Sihyung Park, 2026
- ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Sihyung Park, 2026
- ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Xiaodan Zhou, 2026
- Best Poster Award from the 8th New England Rare Disease Statistics (NERDS) Workshop, Yi Liu, 2025
- FDA-OCE-ASA Oncology Educational Fellowship , Ke Zhu, 2025
- NISS New Researcher Presentation Award from the NISS Virtual New Researchers Conference, Ke Zhu, 2025
- Best Presentation Award from the 38th New England Statistics Symposium, Yi Liu, 2025
- Best Student Poster Award from Workshop on Biostatistics & Bioinformatics, Yi Liu, 2025
- GSA Conferences and Travel Award, North Carolina State University, Shubhajit Sen, 2025
- Gertrude M. Cox Academic Achievement Award, North Carolina State University, Siqi Cao, 2025
- Merck Research Fellowship Award, North Carolina State University, Yi Liu, 2025
- DISS Best Poster Award from Duke Industry Statistics Symposium, Yi Liu, 2025
- IMS Hannan Graduate Student Travel Award from the Institute of Mathematical Statistics, Yi Liu, 2025
- GSA Conferences and Travel Award, North Carolina State University, Yi Liu, 2024
- Student Paper Competition Award from the LiDA Conference, Chenyin Gao, 2025
- 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
- ICSA Student Paper Award, ICSA Applied Statistics Symposium, Chenyin Gao, 2024
- ENAR RAB Student Poster Award Competition, 2nd place, ENAR Spring Meeting, Siyi Liu, 2024
- FDA-OCE-ASA Oncology Educational Fellowship, Taekwon Hong, 2023
- JSM Student Poster Award (honorable mention) from the ASA Biopharmaceutical Section in cooperation with the FDA Statistical Association, Sarah Fairfax, 2023
- BIOP RISW Student Travel Award from the ASA Biopharmaceutical Section in cooperation with the FDA Statistical Association, Siyi Liu, 2023
- JSM Student Paper Award from the ASA Section on Nonparametric Statistics, Joint Statistical Meetings, Yichi Zhang, 2023
- IBM Student Research Award, the 34th New England Statistics Symposium, Lili Wu, 2021
- DISS Best Poster Award from Duke Industry Statistics Symposium, Joe Zhao, 2021
- JSM Paper Award from the Section on Statistics in Epidemiology, Lili Wu, 2021
- ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Andrew Giffin, 2021
- ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Shuhan Tang, 2021
- ENAR Distinguished Paper Competition Award, ENAR Spring Meeting, Lin Dong, 2020
—> Current Members
Postdoc fellows
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- Ke Zhu (Postdoctoral Fellow)
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- Shoudao Wen (Postdoctoral Fellow)
Ph.D. candidates
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- Mingyan Liu (Ph.D. candidate)
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- Honghong Liu (Ph.D. candidate)
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- Md Abdul Basit (Ph.D. candidate)
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- Amy Carpenter (Ph.D. candidate)
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- Siqi Cao (Ph.D. candidate)
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- Sarah Riegel Fairfax (Ph.D. candidate)
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- Yujing Gao (Ph.D. candidate)
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- Kuan Jiang (Ph.D. candidate)
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- Wenwei Vivia Jiao (Ph.D. candidate)
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- Manting Jin (Ph.D. candidate)
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- Yi Liu (Ph.D. candidate)
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- Sihyung Park (Ph.D. candidate)
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- Mufan Wang (Ph.D. candidate)
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- Anqi Zhao (Ph.D. candidate)
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- Xiaodan Zhou (Ph.D. candidate)
Master candidates
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- Charles Harding (Master)
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- Hairong Huang (Master)
—> Alumni and Dissertation Involving Causal Inference Research
Previous postdoc fellows
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- Guangcai Mao (Postdoctoral Fellow, 2023-2025)
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- Hyemin Yeon (Postdoctoral Fellow, 2023-2024)
Previous Ph.D. candidates
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- Shubhajit Sen (Ph.D., Eli Lilly)
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- Yuwen Cheng (Ph.D., Amgen)
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- Jianing Chu (Ph.D., Amazon)
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- Chenyin Gao (Ph.D., Harvard)
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- Siyi Liu (Ph.D.)
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- Taekwon Hong (Ph.D., FDA)
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- Tanchumin Xu (Ph.D., Wells Fargo)
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- Yichi Zhang (Ph.D. AP@IUB)
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- Yunshu Zhang (Ph.D. Postdoc at UPenn)
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- Joe Zhao (Ph.D. SAS Institute)
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- Eunah Cho (Ph.D. LG Display)
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- Yiming Wang (Ph.D. Pinterest)
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- Steven Xu (Ph.D. DoorDash)
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- Dasom Lee (Ph.D. United Therapeutics)
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- Lili Wu (Ph.D. Microsoft)
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- Can Cui (Ph.D. Eli Lilly)
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- Miao Yu (Ph.D. Meta)
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- Nathan Corder (Ph.D. Eli Lilly)
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- Andrew Giffin (Ph.D. FDA)
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- Lin Dong (Ph.D. Google)
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- Qian Guan (Ph.D. Facebook)
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Ph.D. CO-Advisor with Rui Song (2021–2025) Thesis: Advanced causal inference for addressing modern challenges: informative sampling, privacy constraints, and point processes [link] |
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Ph.D. Advisor (2019–2024) Thesis: Advanced statistical causal inference and individualized treatment regime learning: Data Integration, Inference, and Matching. [link] |
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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] |
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Ph.D. Advisor (2019–2024) Thesis: Advanced Statistical Methods for Data Integration and Tensor Completion in Causal Inference. [link] |
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Ph.D. Advisor (2019–2024) Thesis: Treatment effect evaluation in longitudinal studies with missing data and data integration. [link] |
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Ph.D. Co-Advisor with Wenbin Lu (2019–2024)
Thesis: Analysis of Irregularly Spaced Longitudinal Market Transaction Data. [link] |
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Ph.D. Committee Member (2018–2023)
Thesis: Advances in causal inference and the study of interlocus gene conversion [link] |
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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] |
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Ph.D. Advisor (2018–2023)
Thesis: Doubly robust estimators of causal effects in observational studies: theory and practice [link] |
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Ph.D. Co-Advisor with Emily Hector (2018–2023)
Thesis: Advances in Matching Methods for Causal Inference with Multiple Treatments [link] |
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Ph.D. Advisor (2016–2022)
Thesis: Robust Causal Inference Methods for Using Randomized Clinical Trial and Observational Study [link] |
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Ph.D. Committee Member (2017–2022)
Thesis: Covariance Function Estimation and Causal Inference Methods [link] |
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Ph.D. Co-Advisor with Brian Reich (2017–2022)
Thesis: Advances in Semiparametric Quantile Regression [link] |
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Ph.D. Co-Advisor with Sujit Ghosh (2017–2022)
Thesis: Semiparametric Inference of Randomized Controlled Trials and Observational Studies [link] |
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Ph.D. Advisor (2016–2021)
Thesis: Spatially Varying and Multi-Source Data Integrative Causal Inference [link] |
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Ph.D. Committee Member (2017–2021)
Thesis: Advanced Methods in Bayesian Variable Selection and Causal Inference [link] |
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Ph.D. Committee Member (2017–2021)
Thesis: Online Testing and Semiparametric Estimation of Complex Treatment Effect [link] |
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Ph.D. Co-Advisor with Marie Davidian (2014–2021)
Thesis: Conducting Causal Inference on Partially Observed Data via Imputation and Matching [link] |
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Ph.D. Co-Advisor with Brian Reich (2016–2020)
Thesis: Methods for Causal Inference on Spatial Data with Environmental and Public Health Applications [link] |
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Ph.D. Co-Advisor with Eric Laber (2015–2019)
Thesis: Semiparametric Methods for Decision Making and Causal Effect Generalization [link] |
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Ph.D. Committee Member (2015–2019)
Thesis: Bayesian Methods for Optimal Treatment Allocation and Causal Inference [link] |