Jason Gibbons is a health economist at Brigham and Women’s Hospital and Harvard Medical School. His research focuses on causal machine learning in pharmacoepidemiology and on questions related to pharmaceutical market competition. He works at the intersection of causal inference and machine learning, developing and applying approaches that support rigorous observational analyses of medications in real-world data. Within pharmacoepidemiology, his interests include estimating treatment effects while accounting for confounding and heterogeneity across patient populations. His work also examines how competitive dynamics in pharmaceutical markets can influence pricing, access, and adoption of therapies. As a 2026 MAPCI Fellow, Gibbons joins a community focused on advancing rigorous methods for intervention development and evaluation in cancer prevention and control.