4 papers · 1 filter
Bayesian Causal Forests for Zero-Inflated Continuous Outcomes
Hugo Gobato Souto, Eric Wang, Igor Shuryak
Estimating (heterogeneous) treatment effects is still challenging when the outcome is semicontinuous (i.e., exhibiting both a discrete point mass at zero and a skewed continuous di…
Wasserstein Causal Forests for Distribution-Valued Outcomes
Hugo Gobato Souto
This paper proposes Wasserstein Causal Forests (WCF) for settings in which each unit's outcome is itself a probability distribution. This study also defines finite-grid transformed…
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
Hugo Gobato Souto, Francisco Louzada Neto
This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, add…
K-Fold Causal BART for CATE Estimation
Hugo Gobato Souto, Francisco Louzada Neto
This research aims to propose and evaluate a novel model named K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART) for improved estimation of Average Treatment Ef…