Lan Wen

I am an Assistant Professor in the Department of Statistics and Actuarial Science, and a Visiting Scientist in the CAUSALab at the Harvard T.H. Chan School of Public Health.

My primary areas of research to date have been on the development and application of statistical methods in causal inference and the analysis of observational studies where complications can arise due to model misspecification, time-varying confounding and censoring/missing data. I believe that the development of novel methodologies should be rooted in practical applications. Thus, motivated by real-life applications in public health and medicine, my research addresses methodological and conceptual challenges that scientists may face in answering clinically relevant questions using real-world data studies. Challenges that arise in these observational studies include lack of randomization to the strategies of interest, and/or subjects dropping out of the study due to unknown reasons.

• September 2018 - December 2021, Postdoctoral fellow, Harvard T.H. Chan School of Public Health

• October 2014 - August 2018, PhD in Biostatistics, University of Cambridge

• September 2012 - September 2013, Master of Mathematics (Statistics), University of Waterloo

• STAT 231 - Statistics
  • Taught in 2022, 2024, 2025, 2026
• STAT 331 - Applied Linear Models
  • Taught in 2022, 2023
• STAT 431 - Generalized Linear Models and their Applications
  • Taught in 2024
• STAT 831 - Generalized Linear Models and Applications
  • Taught in 2024

* Only courses taught in the past 5 years are displayed.

• Wen, L., & McGee, G.. (2025). Estimating Average Causal Effects with Incomplete Exposure and Confounders. arXiv preprint arXiv:2506.21786.

• Wen, L., & Sarvet, A. L.. (2025). Estimating average treatment effects when treatment data are absent in a target study. arXiv preprint arXiv:2509.22543.

• Wen, L., Steingrimsson, J. A., Robertson, S. E., & Dahabreh, I. J.. (2025). Multi-source analyses of average treatment effects with failure time outcomes: L. Wen et al. Lifetime Data Analysis, 1-29. Springer.

• Wen, L., Sarvet, A. L., & Young, J. G.. (2025). On treating right-censoring events like treatments. arXiv preprint arXiv:2511.17379.

• Chiu, Y. ‐H., & Wen, L.. (2025). Identification and Estimation of the Average Causal Effects Under Dietary Substitution Strategies. Statistics in Medicine, 44(5), e70007. John Wiley & Sons, Inc. Hoboken, USA.

• Wen, L., Sarvet, A. L., & Stensrud, M. J.. (2024). Causal effects of intervening variables in settings with unmeasured confounding. Journal of Machine Learning Research, 25(345), 1-54.

• Sewak, A., Lodi, S., Li, X., Shu, D., Wen, L., Mayer, K. H., Krakower, D. S., et al. (2024). Causal effects of stochastic PrEP interventions on HIV incidence among men who have sex with men. American journal of epidemiology, 193(1), 6-16. Oxford University Press.

• Wanis, K. Nashat, Sarvet, A. L., Wen, L., Block, J. P., Rifas-Shiman, S. L., Robins, J. M., & Young, J. G.. (2024). Grace periods in comparative effectiveness studies of sustained treatments. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(3), 796-810. Oxford University Press UK.

• Steingrimsson, J. A., Wen, L., Voter, S., & Dahabreh, I. J.. (2024). Interpretable meta-analysis of model or marker performance. arXiv preprint arXiv:2409.13458.

• Chiu, Y. - H., Wen, L., McGrath, S., Logan, R., Dahabreh, I. J., & Hernán, M. A.. (2023). Evaluating model specification when using the parametric g-formula in the presence of censoring. American journal of epidemiology, 192(11), 1887-1895. Oxford University Press.

• Sarvet, A. L., Stensrud, M. J., & Wen, L.. (2023). Interpretational errors in statistical causal inference. arXiv preprint arXiv:2312.07610.

• Wen, L., Marcus, J. L., & Young, J. G.. (2023). Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis. Statistical methods in medical research, 32(3), 509-523. SAGE Publications Sage UK: London, England.

• Wen, L., Hernán, M. A., & Robins, J. M.. (2022). Multiply robust estimators of causal effects for survival outcomes. Scandinavian Journal of Statistics, 49(3), 1304-1328.

• Wanis, K. Nashat, Sarvet, A. L., Wen, L., Block, J. P., Rifas-Shiman, S. L., Robins, J. M., & Young, J. G.. (2022). The role of grace periods in comparative effectiveness studies of different medications. arXiv e-prints, arXiv: 2212.11398.

• Katsoulis, M., Lai, A. G., Diaz-Ordaz, K., Gomes, M., Pasea, L., Banerjee, A., Denaxas, S., et al. (2021). Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records. The Lancet Diabetes & Endocrinology, 9(10), 681-694. Elsevier.

• Wen, L., Young, J. G., Robins, J. M., & Hernán, M. A.. (2021). Parametric g-formula implementations for causal survival analyses. Biometrics, 77(2), 740-753. Oxford University Press.