This project examines how mandatory diversity statements in faculty job applications influence hiring and student outcomes. Using comprehensive text from JOE and APSA postings linked to institution–year–discipline hiring records, we classify DEI-related requirements and estimate their effects using staggered-adoption DiD estimators with entropy-balancing weights.

This paper compares causal forests with varying-coefficient penalized spline estimators for heterogeneous treatment effects. We conduct simulations with randomized treatment assignment that vary heterogeneity, predictor dimension, and discrete nuisance structure. Performance is measured by out-of-sample mean squared error. Forests perform best in Wager–Athey designs with sharp treatment-effect transitions. Penalized splines dominate when heterogeneity is smooth, when the baseline mean and treatment effect differ in structure, and in mixed designs with continuous, binary, and group-level covariates. As group cardinality rises, forest performance deteriorates, while mgcv random-effect smooths handle group structure effectively and remain easy to implement.

We develop a statistical framework for diagnosing parameter identifiability and uncertainty in stochastic agent-based models (ABMs). The approach combines Monte Carlo experiments with simulation-based confidence intervals, providing generalizable tools for calibration, validation, and sensitivity analysis in complex ABMs.

This project examines how the expansion of dollar stores and low-nutrition retail environments contribute to obesity and metabolic health risks. Using store rollouts and quasi-experimental variation in food environments, I study how changes in access to calorie-dense, nutrient-poor options affect chronic disease outcomes.