Title: 

Heterogeneous Treatment Effects in Panel Data: Applications to the Healthy Incentives Program

Abstract:

In this talk, we will discuss work motivated by studying the Healthy Incentives Program (HIP), a food-subsidy program. Our goal is to quantify how adding new vendors affects program utilization using observational panel data. In particular, the effects may be heterogeneous, and the timing of the interventions may be highly irregular. This is an instance of a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns.

To address this problem, we introduce the Panel Clustering Estimator (PaCE). PaCE partitions observations into clusters with similar treatment effects using a regression tree and leverages the low-rank structure of the panel data to estimate the average treatment effect within each cluster. Our theoretical results identify conditions on the treatment patterns under which the treatment effects are recoverable, and we establish convergence guarantees under those conditions. Computational experiments show that PaCE achieves higher accuracy than existing approaches while remaining interpretable. Applying PaCE to HIP data, we identify the heterogeneous impacts of vendor additions on HIP utilization across Massachusetts ZIP codes and uncover key demographic and contextual factors driving these differences. Our findings provide valuable insights for future budget planning and for identifying which ZIP codes to target with vendor additions.

Bio:

Emily Zhang is a fifth-year PhD candidate at the MIT Operations Research Center, advised by Professors Retsef Levi and Georgia Perakis. Her research addresses critical challenges in food systems, focusing on reducing food waste and improving equitable access to healthy food. Her work spans modeling, optimization, causal inference, inventory management, and data-driven operations, and has been conducted in collaboration with the Massachusetts Department of Transitional Assistance and nonprofit organizations such as Met Council. Prior to her PhD, she earned dual B.S. degrees in Computer Science and Mathematics from MIT.