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

TL;DR

This paper presents a nonparametric ps-BART model for estimating ATE and CATE with continuous treatments, improving flexibility and accuracy over existing models, especially in nonlinear scenarios.

Contribution

It introduces the ps-BART model, a flexible nonparametric approach that outperforms Bayesian Causal Forests in estimating treatment effects with continuous treatments.

Findings

ps-BART outperforms BCF in nonlinear settings

robustness in uncertainty estimation

accurate point-wise and probabilistic estimates

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.