Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments

2024/09/10 by Hugo Gobato Souto, Souto, Hugo Gobato, Francisco Louzada Neto +1
Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2409.06593

openalex publication_date 2024/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables. Across three distinct sets of Data Generating Processes (DGPs), the ps-BART model consistently outperforms the BCF model, particularly in highly nonlinear settings. The ps-BART model's robustness in uncertainty estimation and accuracy in both point-wise and probabilistic estimation demonstrate its utility for real-world applications. This research fills a crucial gap in causal inference literature, providing a tool better suited for nonlinear treatment-outcome relationships and opening avenues for further exploration in the domain of continuous treatment effect estimation.

Related

• K-Fold Causal BART for CATE Estimation
• A General Bayesian Nonparametric Approach for Estimating Population-Level and Conditional Causal Effects
• Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
• A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches
• A generalized definition of the average causal effect for both binary and continuous treatments