arXiv subjects

Jared D. Fisher

Publications and source records attributed to Jared D. Fisher.

4 recordsLinked to original sources

arXiv2026

Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensembles

Estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing flexible machine learning methods are sensitive to the weak instruments problem and can yield unstable estimates of heterogeneity when run repeatedly with different initialization or random seeds. Our main methodological contribution is to present a Bayesian Causal Forest model for binary response variables in scenarios with noncompliance. By repeatedly imputing individuals' compliance types, we can flexibly estimate heterogeneous treatment effects among compliers. Simulation studies demonstrate the usefulness of our approach when compliance and treatment effects are heterogeneous. We use this method to detect and analyze heterogeneity in the treatment effects in the Illinois Workplace Wellness Study, which not only features heterogeneous and one-sided compliance but also several binary outcomes of interest. We focus on three outcomes one year after intervention. We confirm a null effect on the presence of a chronic condition, discover meaningful heterogeneity in the impact of the intervention on metabolic parameters though the average effect is null in classical partial effect estimates, and find heterogeneity in the intervention's effect on individuals' perception of management prioritization of health and safety.

stat.AP↗

arXiv2026

Bayesian Multinomial Logistic Regression for Numerous Categories

Bayesian multinomial logistic regression provides a principled, interpretable approach to multiclass classification, but posterior sampling becomes increasingly expensive as the model dimension grows. Prior work has studied scalability in the number of subjects and covariates; in contrast, this paper focuses on how computation changes as the number of outcome categories increases. To improve scalability in settings with numerous categories, we adapt a gamma-augmentation strategy to decouple category-specific coefficient updates, so that each category's coefficients can be updated conditional on a single auxiliary variable per subject, rather than on the full set of other categories' coefficients. Because the resulting coefficient conditionals are non-conjugate, we couple this augmentation with either adaptive Metropolis-Hastings or elliptical slice sampling. Through simulation and a real-data example, we compare effective sample size and effective sampling rate across several standard competitors. We find that the best-performing sampler depends on the dimension and imbalance regime, and that the proposed augmentation provides substantial speedups in scenarios with numerous categories.

stat.CO↗

arXiv2025

Probit Monotone BART

Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary.

stat.ML↗

arXiv2023

Improving the Aggregation and Evaluation of NBA Mock Drafts

Many enthusiasts and experts publish forecasts of the order players are drafted into professional sports leagues, known as mock drafts. Using a novel dataset of mock drafts for the National Basketball Association (NBA), we analyze authors' mock draft accuracy over time and ask how we can reasonably use information from multiple authors. To measure how accurate mock drafts are, we assume that both mock drafts and the actual draft are ranked lists, and we propose that rank-biased distance (RBD) of Webber et al. (2010) is the appropriate error metric for mock draft accuracy. This is because RBD allows mock drafts to have a different length than the actual draft, accounts for players not appearing in both lists, and weights errors early in the draft more than errors later on. We validate that mock drafts, as expected, improve in accuracy over the course of a season, and that accuracy of the mock drafts produced right before their drafts is fairly stable across seasons. To be able to combine information from multiple mock drafts into a single consensus mock draft, we also propose a ranked-list combination method based on the ideas of ranked-choice voting. We show that our method provides improved forecasts over the standard Borda count combination method used for most similar analyses in sports, and that either combination method provides a more accurate forecast over time than any single author.

stat.AP↗