Fei Fang

Title: Assistant Professor of Quantitative Methods in Management
Department: Decision and Information Sciences
Office: 302 Elliott Hall
Phone: (248) 370-4966
Email: [email protected]

Education:
Ph.D. in Decision Sciences, Duke University, Fuqua School of Business
M.A. in Statistics, Boston University

Biography:
Fei Fang is an Assistant Professor of Quantitative Methods in Management in the Department of Decision and Information Sciences at Oakland University. Her research focuses on causal inference under interference, causal discovery, and causal methods in medicine and the social sciences. Previously, she was a Postdoctoral Associate in the Department of Biostatistics at Yale University. She earned her Ph.D. in Decision Sciences from the Fuqua School of Business at Duke University and her M.A. in Statistics from Boston University.

Teaching interests: 
Quantitative methods; statistics; causal inference

Research interests: 
Causal inference under interference; causal discovery; causal methods in medicine and the social sciences

Journals and Articles:

Theory and Methodology

Fang, F., & Forastiere, L. (2025). Design-based weighted regression estimators for average and conditional spillover effects. | Regression based method for spillover effect Slides

Bai, S., Fang, F., & Wang, T. (2025). Structural causal models for extremes: An approach based on exponent measures.

Fang, F., Airoldi, E. M., & Forastiere, L. (2025). Inward and outward spillover effects of one unit’s treatment on network neighbors under partial interference. Biometrika, major revision. | Inward and Outward comparison slides

Jiang, C., Fang, F., Talbot, D., & Schnitzer, M. E. (2025). Estimating direct and spillover vaccine effectiveness with partial interference under test-negative sampling. medRxiv.

Belloni, A., Fang, F., & Volfovsky, A. (2022). Neighborhood adaptive estimators for causal inference under network interference.

Fang, F., Sussman, D. L., & Lyzinski, V. (2018). Tractable graph matching via soft seeding.

Fang, F., Sun, Y., & Spiliopoulos, K. (2017). On the effect of heterogeneity on flocking behavior and systemic risk. Statistics & Risk Modeling, 34(3–4), 141–155.

Applications

Wu, Y., & Fang, F. (2025). Association between antihypertensive medication use and steatotic liver disease with liver fibrosis and mortality among U.S. adults. Clinical and Experimental Hypertension.

Arlotto, A., Belloni, A., Fang, F., & Pekeč, S. (2024). Ballot design and electoral outcomes: The role of candidate order and party affiliation. Journal of the American Statistical Association, major revision.

Media coverage: Duke Fuqua Insights — How Ballot Design May Decide Elections

Wu, Y., Fang, F., Fan, X., & Nie, H. (2024). Associations of cannabis use, metabolic dysfunction–associated steatotic liver disease, and liver fibrosis in US adults. Cannabis and Cannabinoid Research.

Wu, Y., & Fang, F. (2024). Validating the new nomenclature of steatotic liver disease in patients with excessive alcohol intake. The Lancet Gastroenterology & Hepatology, 9(5), 409. Article

Other Publications

Fang, F., & Forastiere, L. (2025). Discussion on “Causal inference with misspecified network interference structure” by Bar Weinstein and Daniel Nevo. Biometrics.