用流模型联合学习表示与因果效应，提升预测准确性。
RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation
- 将表示学习与条件流匹配结合，统一优化因果估计
- 在多个基准上同时优于现有方法的点估计与分布估计
- 适合需要精准因果推断的医疗、经济等领域研究者
从观测数据中估计因果效应在医疗、经济和社会政策等领域的应用日益重要。因果推断的核心挑战来自反事实缺失和选择偏差。现有方法多局限于点估计，缺乏分布建模能力。本文提出RepFlow，一种新框架，将因果效应估计建模为表示学习与条件流匹配（CFM）的联合优化问题。通过最小化处理组与对照组表示之间的熵正则化Wasserstein距离，缓解选择偏差；引入隐变量的$ L_2 $归一化约束以增强数值稳定性。该平衡表示使流模型能准确捕捉潜在结果分布。在多个基准上的大量实验表明，RepFlow在点估计和分布估计上均持续优于现有方法。
原文摘要 · Abstract (English)
Estimating causal effects from observational data has become increasingly critical in diverse fields including healthcare, economics, and social policy. The fundamental challenge in causal inference arises from the missing counterfactuals and the selection bias. Existing methods are largely limited to point estimates and lack the capacity for distribution modeling. In this work, we propose RepFlow, a novel framework that formulates causal effect estimation as a joint optimization problem integrating representation learning with Conditional Flow Matching (CFM). RepFlow mitigates selection bias by minimizing the entropically regularized Wasserstein distance between treated and control representations. To enhance numerical stability, we further introduce an $L_2$ normalization constraint on latent representations. This balanced representation enables the flow model to accurately capture the distribution of potential outcomes. Extensive experiments across a wide range of benchmarks demonstrate that RepFlow consistently outperforms existing methods in both point and distributional causal 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。