无需先验知识，用高斯过程解决连续处理下的混淆偏倚问题
Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding
- 基于通用离散化构建潜变量框架，逼近任意因果模型的观测与干预分布
- 在仅依赖处理与结果时间顺序条件下，实现对连续处理效应的鲁棒评估
- 适用于缺乏详细环境信息的政策评估场景，尤其适合复杂连续决策分析
混淆偏倚是政策评估中的关键挑战，因观测数据无法唯一识别目标因果效应（即参数未定）。现有抗混淆方法通常需详尽的环境先验知识，或仅适用于离散处理与结果。本文研究在存在混淆时，从观测数据中对连续域上的因果效应进行评估，仅需处理与结果之间的基本时间顺序。提出一种外生域的通用离散化方法，可利用有限个潜状态以任意精度逼近任意因果模型的观测与干预分布。基于此通用逼近性质，开发了一类新型因果高斯过程（Causal Gaussian Process, CGP）模型，能有效近似具有混淆观测的任意因果模型的观测与干预分布。
原文摘要 · Abstract (English)
The presence of confounding bias poses a key challenge in policy evaluation, as the target causal effects of actions are not identifiable (i.e., underdetermined) from observational data. On the other hand, existing confounding-robust evaluation strategies require detailed prior knowledge about the environment or apply only to discrete treatments and outcomes. This paper investigates causal effect evaluation over the continuous domain from confounded observations, while requiring only basic temporal ordering between the treatment and the outcome. We introduce a universal discretization of the exogenous domains that approximates the observational and interventional distributions of any causal model with arbitrary accuracy using a finite number of latent states. Building on this newfound universal approximation property, we develop a novel family of Causal Gaussian process (CGP) models that effectively approximate the observational and interventional distributions of any causal model with confounded observations.
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。