提出非参数方法精准估计连续处理效应，突破传统模型局限。
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
- 基于非参数框架构建ps-BART模型，灵活捕捉处理与结果的非线性关系。
- 在三类生成数据中均优于BCF模型，尤其在高度非线性场景下表现更优。
- 适合处理复杂非线性关系的因果推断研究者，提升不确定性估计可靠性。
本文提出一种广义的ps-BART模型，用于连续处理情境下的平均处理效应（ATE）和条件平均处理效应（CATE）估计，解决了贝叶斯因果森林（BCF）模型的局限性。该模型具有非参数特性，能灵活建模处理变量与结果变量间的非线性关系。在三种不同的数据生成过程（DGPs）下，ps-BART模型始终优于BCF模型，尤其在高度非线性设定中表现突出。其在点估计和概率估计中的准确性以及稳健的不确定性量化能力，表明该方法在真实世界应用中的实用性。本研究填补了因果推断文献中关于连续处理效应估计的重要空白，为非线性处理-结果关系提供了更优工具，并为后续研究开辟新路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。