3 papers
stat.ME2025
Forests for Differences: Robust Causal Inference Beyond Parametric DiD
Hugo Gobato Souto, Francisco Louzada Neto
This paper introduces the Difference-in-Differences Bayesian Causal Forest (DiD-BCF), a novel non-parametric model addressing key challenges in DiD estimation, such as staggered ad…
stat.ML2024
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…
stat.ML2024
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…