推进因果推断：一种用于连续处理的 ATE 和 CATE 估计的非参数方法
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
AI总结：
提出广义 ps-BART 非参数模型估计连续处理下的 ATE 与 CATE，在多组 DGP 中优于 BCF，尤其适用于非线性关系。
摘要：
本文介绍了一种广义 ps-BART 模型，用于在连续处理情形下估计平均处理效应（ATE）和条件平均处理效应（CATE），以解决 Bayesian Causal Forest（BCF）模型的局限性。ps-BART 模型的非参数特性使其能够灵活捕捉处理变量与结果变量之间的非线性关系。在三组不同的数据生成过程（DGPs）中，ps-BART 模型始终优于 BCF 模型，尤其是在高度非线性的设置中。ps-BART 模型在不确定性估计方面的稳健性，以及在逐点估计和概率估计中的准确性，证明了其在现实世界应用中的实用性。本研究填补了因果推断文献中的一个关键空白，提供了一种更适合非线性处理—结果关系的工具，并为连续处理效应估计领域的进一步探索开辟了途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
