提出新方法在部分有向图中保持因果效应估计的一致性。
Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls
- 用强d-凸包识别可折叠的变量集，确保估计一致性。
- 算法可高效求解图中可折叠集合，在真实数据上验证有效。
- 适合从事因果推断与图模型研究者参考。
本文提出一种在已完成的部分有向无环图（CPDAG）中保持因果效应估计一致性的可折叠方法。首先定义了CPDAG上的估计可折叠性，并将最小可折叠集合刻画为强d-凸包。设计了高效算法用于在有向无环图（DAG）中求解此类集合，并推广至CPDAG。随后，将图简化过程与IDA框架结合。实验与实证分析表明，该方法在CPDAG中对因果估计具有显著有效性。代码已公开于 https://github.com/Jamyang-D/strongly-convex。
原文摘要 · Abstract (English)
This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs). We first introduce the estimate collapsibility for CPDAGs and characterize the minimal collapsible sets as strong d-convex hulls. An efficient algorithm is devised to obtain such sets in DAGs and is generalized to CPDAGs. Then, we combine the graph reduction procedure with the IDA framework. Finally, experiments and empirical analysis show the effectiveness of the collapsibility for causal estimations in CPDAGs. Code is available at https://github.com/Jamyang-D/strongly-convex.
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。