9 papers
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…
When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels
Robson Tigre, Hugo Gobato Souto
Large language models are increasingly used to simulate consumers, yet agreement with aggregate survey outcomes can conceal errors in the subgroup estimates that guide decisions. W…
A Mathematical Framework for Topological Causal Data Analysis
Hugo Gobato Souto, Ioannis Diamantis
Many modern outcomes, including images, point clouds, networks, and spatial fields, are structured objects for which \(Y^1-Y^0\) may be undefined or scientifically inadequate. We i…
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…
Ablation Studies for Novel Treatment Effect Estimation Models
Hugo Gobato Souto, Francisco Louzada
Ablation studies are essential for understanding the contribution of individual components within complex models, yet their application in nonparametric treatment effect estimation…
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…
Beyond Arbitrary Replications: A Principled Approach to Simulation Design in Causal Inference
Hugo Gobato Souto, Francisco Louzada Neto
Evaluation of novel treatment effect estimators frequently relies on simulation studies lacking formal statistical comparisons and using arbitrary numbers of replications (). Th…