I’m Vittorio, a PhD student studying statistics at Duke under the supervision of Alex Volfovsky, Cynthia Rudin, and Jared Murray. I create methods in causal inference and Bayesian nonparametrics that: 1. can be used in practice, featuring scalable implementations that facilitate their application to real data; 2. are designed to handle the complexity inherent in real data without making naive assumptions; and 3. have exceptional predictive accuracy, even as they boast other desirable features like interpretability and uncertainty quantification.
PhD in Statistical Science (Expected), 2022
Bachelor's in Statistics, 2018
We find that receiving multiple transplants for pediatric patients is associated with increased post-transplant mortality, but not increased acute rejection episodes.
We propose a method for matching in the presence of network interference. To reduce bias, we match units with similar neighborhood graphs, prioritizing similarity in subgraphs learned to be more important.
We match units via axis-aligned hyper-boxes that encompass space where the treatment effect is nearly constant. The resulting treatment effect estimates are therefore both case-based and interpretable.
We propose a latent-variable extension to BART that is capable of performing highly-flexible density regression. We target estimation of quantile treatment effects and apply our methodology to a real-world example estimating returns to education.