CausAI Lab

Causal Artificial Intelligence (CausAI Lab)


660 South Euclid Ave


St. Louis, MO 63110

Linying Zhang is an Assistant Professor of Biostatistics at the Institute for Informatics, Data Science and Biostatistics at Washington University School of Medicine in St. Louis where she leads the CausAI Lab to integrate causality with artificial intelligence (AI) to achieve reliable real-world evidence (RWE) generation in clinical research and equitable clinical AI for all.

Lab mission

AI has the potential to revolutionize healthcare if developed and deployed thoughtfully. In healthcare, AI models are frequently trained on real-world data (RWD) to generate real-world evidence (RWE) or assist clinical decision-making. However, RWD are intricate, imperfect, and reflects existing health disparities. To leverage AI effectively, it’s vital to develop methodologies that address and correct the biases inherent in RWD. Our lab is committed to integrating the disciplines of causality and artificial intelligence to generate reliable real-world evidence and to create equitable clinical AI models that serve all patient groups.

Join us

Post-doc Fellows We are looking for one postdoctoral fellow to work on causal AI for health care.

PhD Students We are looking for 1-2 PhD students starting Fall 2024. Please apply directly to the Biomedical Informatics and Data Science (BIDS) PhD program at Washington University School of Medicine. In your PhD application, please explicitly mention your interest in working with Professor Linying Zhang. Existing BIDS and Computational & Systems Biology (CSB) PhD students interested in rotating through the lab should email Dr. Zhang directly.

Undergraduates or Master’s Students Undergraduates and Master’s students looking for research opportunities are encouraged to apply through the BIDS@I2 Summer Research Internship. We are looking for students who have taken at least one machine learning course and received a good grade. For masters students, we typically expect students to have taken a graduate-level machine learning course and a graduate-level probability or statistical inference course, or have had significant related research experience. WashU students interested in research assistantship should email Dr. Zhang directly.


Jul 12, 2024 Call for Submission to the workshop on reliable and equitable RWE at AIME 2024! Deadline is May 31, 2024.
May 7, 2024 I will present our work on “Building Causally Explainable Fair Learning Health System” at SAIL 2024!
Feb 5, 2024 Our paper Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation was accepted to the Conference on AI for Medicine, Health, and Care (AIMHC).
Dec 10, 2023 I will be the senior chair at the causality in health roundable at Machine Learning for Health (ML4H)!
Nov 13, 2023 I will give a presentation at AMIA Symposium 2023 on health disparities with causality!

Selected publications

  1. cai2024similar.png
    Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An OHDSI Network Study
    Cindy X. Cai, Akihiko Nishimura, Mary G. Bowring, Erik Westlund, Diep Tran, Jia H. Ng, Paul Nagy, Michael Cook, Jody-Ann McLeggon, Scott L. DuVall, and 27 more authors
    Ophthalmology Retina, 2024
  2. zhang2024evaluating.png
    Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation
    Linying Zhang, Lauren R. Richter, Tevin Kim, and George Hripcsak
    Jan 2024
  3. zhang2019medical.png
    The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
    Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, and George Hripcsak
    In Proceedings of the 4th Machine Learning for Healthcare Conference, Jan 2019
  4. zhang2022adjusting.png
    Adjusting for Indirectly Measured Confounding Using Large-Scale Propensity Score
    Linying Zhang, Yixin Wang, Martijn J. Schuemie, David M. Blei, and George Hripcsak
    Journal of Biomedical Informatics, Jan 2022
  5. zhang2024causal.png
    Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
    Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, and George Hripcsak
    Jan 2024