用条件密度估计提升个体治疗效应预测精度，区间更窄更实用。
Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates
- 基于结果变量的条件密度构建评分函数，改进传统方法
- 预测区间在保证有效性的同时显著变窄，优于现有方法
- 适合医疗、政策等需精准个体化决策的领域
在数据日益复杂多样的时代，精准预测个体治疗效应（ITE）在医疗、经济与公共政策等领域愈发关键。当前最先进的方法虽可通过合规分位数回归（CQR）等技术提供有效的预测区间，但常导致过度保守的结果。本文提出一种基于给定协变量下结果变量条件密度的合规推断方法，采用参考分布技术，在两阶段合规ITE框架中高效估计条件密度作为得分函数。我们证明所获预测区间不仅边际上有效，且比现有方法更窄。实验结果进一步验证了该方法的有效性与实用性。
原文摘要 · Abstract (English)
In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and public policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal Quantile Regression (CQR) and related techniques, often yield overly conservative prediction intervals. In this work, we introduce a conformal inference approach to ITE using the conditional density of the outcome given the covariates. We leverage the reference distribution technique to efficiently estimate the conditional densities as the score functions under a two-stage conformal ITE framework. We show that our prediction intervals are not only marginally valid but are narrower than existing methods. Experimental results further validate the usefulness of our method.
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。