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

arXiv: https://arxiv.org/abs/2409.06593 · HTML: https://mlsanity.com/paper/2409.06593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Ablation Studies for Novel Treatment Effect Estimation Models](https://mlsanity.com/paper/2410.15560/) (2024)

## Abstract (arXiv)

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.