7 papers
Seeing the Forest for the Trees: The Gaussian Process Limit of BART
Cory McCartan, Melody Huang
Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous theoretical work has attempted to expla…
The Role of Confounders and Linearity in Ecological Inference: A Reassessment
Shiro Kuriwaki, Cory McCartan
Estimating conditional means using only the marginal means available from aggregate data is known as the ecological inference problem. We reassess this literature, arguing that it…
Identification and Semiparametric Estimation of Conditional Means from Aggregate Data
Cory McCartan, Shiro Kuriwaki
We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of…
Relative Bias Under Imperfect Identification in Observational Causal Inference
Melody Huang, Cory McCartan
To conduct causal inference in observational settings, researchers must rely on certain identifying assumptions. In practice, these assumptions are unlikely to hold exactly. This p…
Generalized Sequential Monte Carlo Sampling for Redistricting Simulation
Philip O'Sullivan, Kosuke Imai, Cory McCartan
Simulation methods have become important tools for quantifying partisan and racial bias in redistricting plans. We generalize the Sequential Monte Carlo (SMC) algorithm of McCartan…
Gerrymandering and geographic polarization have reduced electoral competition
Ethan Jasny, Christopher T. Kenny, Cory McCartan +8
Changes in political geography and electoral district boundaries shape representation in the United States Congress. To disentangle the effects of geography and gerrymandering, we…