Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
<|reference_start|>Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments: 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.<|reference_end|>
@article{souto2024advancing,
    title={Advancing Causal Inference: A Nonparametric Approach to ATE and CATE
  Estimation with Continuous Treatments},
    author={Hugo Gobato Souto and Francisco Louzada Neto},
    journal={arXiv preprint arXiv:2409.06593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06593},
    primaryClass={stat.ML cs.LG stat.AP}
}