Publications

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Density Regression with Bayesian Additive Regression Trees

We extend Bayesian Additive Regression Trees to the setting of density regression, resulting in an accurate and efficient sampler with strong theoretical guarantees.

Vittorio Orlandi, Jared Murray, Antonio Linero, and Alexander Volfovsky

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FLAME: Interpretable Matching for Causal Inference

We present the FLAME package for performing fast, accurate matching for observational causal inference.

Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky

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Post-operative Outcomes in Infants Undergoing ABO Incompatible Heart Transplantation in the US

We use Bayesian Additive Regression Trees to analyze data from the United Network for Organ Sharing database. We find that ABO incompatible infant heart transplant does not affect post-transplant survival, incidence of rejection, or postoperative length of stay, and that it therefore remains a viable and important strategy for increasing the size of the infant donor pool.

Chauhan D, Orlandi V, Rajab T K, Bedeir K, Volfovsky A, and Mokashi S

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Outcomes of Cardiac Retransplantation in Pediatric Population in the US: Analysis of UNOS Data

We find that receiving multiple transplants for pediatric patients is associated with increased post-transplant mortality, but not increased acute rejection episodes.

Chauhan D, Orlandi V, Rajab T K, Bedeir K, Volfovsky A, and Mokashi S

Almost Matching Exactly for Treatment Effect Estimation Under Network Interference

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.

M. Usaid Awan*, Marco Morucci*, Vittorio Orlandi*, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky

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Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

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.

Marco Morucci*, Vittorio Orlandi*, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky

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Bayesian Additive Regression Trees With Density Regression for Quantile Treatment Effect Estimation

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.

Vittorio Orlandi, Jared Murray, and Alexander Volfovsky

FLAME: Interpretable Matching for Causal Inference

An R package implementing interpretable matching algorithms for causal inference. These algorithms match units via a learned, weighted Hamming distance that determines which covariates are more important to match on.

Vittorio Orlandi

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