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: 2023 Spring
CIMA Group Meeting: 2022 Spring
Resources
Resources for writing and exams
Members
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- Guangcai Mao (AP)
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- Zhen Chen (Ph.D. candidate)
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- Yuwen Cheng (Ph.D. candidate)
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- Jianing Chu (Ph.D. candidate)
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- Sarah Riegel Fairfax (Ph.D. candidate)
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- Chenyin Gao (Ph.D. candidate)
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- Yujing Gao (Ph.D. Candidate)
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- Wei Ma (Visiting Scholar)
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- Mai Nguyen (Ph.D. candidate)
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- Tawkwon Hong (Ph.D. candidate)
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- Wenwei Vivia Jiao (Ph.D. candidate)
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- Siyi Liu (Ph.D. candidate)
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- Tanchumin Xu (Ph.D. candidate)
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- Yichi Zhang (Ph.D. candidate)
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- Yunshu Zhang (Ph.D. candidate)
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- Joe Zhao (Ph.D. candidate)
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- Xiaodan Zhou (Ph.D. candidate)
Alumni and Thesis involving Causal Inference Research
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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] |
