Project title: The Unmeasured Patient: Observational Data, Causality, and the Errors of Evidence-Based Medicine
Project description: In modern medicine, a rigid hierarchy of evidence has long elevated randomized controlled trials (RCTs) and experimental models above other forms of knowledge. However, diverse forms of observational data—from postmarketing surveillance to anonymous patient narratives—are increasingly shaping how we identify effective treatments, discover rare risks, and understand diagnostic uncertainty from a causal perspective. This project investigates how these nontraditional evidence sources, combined with causal learning enabled by artificial intelligence, can challenge entrenched assumptions in medical knowledge, particularly in areas where traditional constructs of clinical knowledge fall short (such as psychiatry, women’s health, and chronic illness). This work aims to show how rigorous engagement with observational data—both qualitative and quantitative—can reveal structural blind spots in medical knowledge and support more equitable, responsive practices.
Most important achivement: I'd like to think it's yet to come!
Reflections on the College Scholar Program: To me, the only road to meaning is one that traces every question back to the complex system from which it arises. I’m honored to be selected as a College Scholar at an institution that remains true to its founding ethos of “any person, any study” through this intellectually expansive program. It has given me the opportunity to synthesize many distinct models of the world, unbound by the artificial boundaries we often impose in the form of disciplinary siloes.