When-If Decision-Making Using Synthetic Survival Control with Jessy Han

Aoom link: https://uwmadison.zoom.us/j/94900072392?pwd=bbafSnaME0KfVwjRyFH96XHaj4JBHQ.1&from=addon

Abstract: Understanding the impact of decisions on when a target event occurs, not just whether it occurs, is central to many fields, including patient survival in healthcare, criminal recidivism in policy evaluation, and customer churning in business. This when-if decision-making framework integrates causal inference and survival analysis to support decisions with observational data where the timing of the target event is the key quantity of interest and the data are often sparse, censored, or confounded.

This talk is motivated by a setting that concerns evaluating the efficacy of different therapies for T-Cell Lymphoma across a heterogeneous patient population. We discuss Synthetic Survival Control (SSC), a new method for estimating counterfactual hazard trajectories in panel data with censoring and unobserved confounding. Specifically, we extend the traditional panel data literature by utilizing a causal survival panel framework with an underlying low-rank structure that naturally arises under classical parametric survival models. Within this framework, we establish both identification of the causal estimand and finite-sample guarantees for SSC. We also provide full validation of the proposed method through our motivating application in collaboration with clinicians at Massachusetts General Hospital.

Beyond healthcare, in this talk I will also discuss how this when-if framework extends to policy evaluation in criminal justice and customer churn prediction, where understanding the timing of events is crucial for intervention design and resource allocation.

Bio: Jessy (Xinyi) Han is a Ph.D. candidate at Massachusetts Institute of Technology advised by Prof. Devavrat Shah and Prof. Fotini Christia. Her research develops methods in causal inference and survival analysis to enable when–if decision-making, a framework for understanding how interventions affect not only what happens but when it happens. She works closely with practitioners to bring these methods into high-impact domains, including healthcare, policy evaluation, and business strategies. She has been recognized with several honors, including the Google–MIT Schwarzman College of Computing Fellowship. Before MIT, she earned her B.S. from Columbia University in Computer Science with minors in Economics and Applied Mathematics.