在 ADMG 之上的 C-DMG 中识别宏观因果效应
Identifying Macro Causal Effects in a C-DMG over ADMGs
AI 导读
因果效应识别研究通常假定有完整指定的 DAG 或 ADMG，本文研究部分指定的聚类有向混合图（C-DMG）。论文聚焦宏观因果效应——即整个聚类对其他聚类的效应，证明当聚类大小未知或大于 1 时，do-calculus 在 ADMG 之上的 C-DMG 中识别这些效应既可靠又完备，并给出了不可识别性的图刻画。该论文已被 TMLR 接收。
正文 · 原文
Abstract:Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods assume the availability of fully specified directed acyclic graphs or acyclic directed mixed graphs. However, in complex domains such as medicine and epidemiology, complete causal knowledge is often unavailable, and only partial information about the system is accessible. This paper focuses on causal effect identification within partially specified causal graphs, with particular emphasis on cluster-directed mixed graphs (C-DMGs) which can represent many different acyclic directed mixed graphs (ADMGs). These graphs provide a higher-level representation of causal relationships by grouping variables into clusters, offering a more practical approach for handling complex systems. Unlike fully specified ADMGs, C-DMGs can contain cycles, which complicate their analysis and interpretation. Furthermore, their cluster-based nature introduces new challenges, as it gives rise to two distinct types of causal effects: macro causal effects and micro causal effects, each with different properties. In this work, we focus on macro causal effects, which describe the effects of entire clusters on other clusters. We establish that the do-calculus is both sound and complete for identifying these effects in C-DMGs over ADMGs when the cluster sizes are either unknown or of size greater than one. Additionally, we provide a graphical characterization of non-identifiability for macro causal effects in these graphs.
| Comments: | This article with the initial version of the assumption was accepted to TMLR |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2504.01551 [cs.AI] |
| (or arXiv:2504.01551v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2504.01551 arXiv-issued DOI via DataCite |
Submission history
From: Simon Ferreira [view email]
[v1]
Wed, 2 Apr 2025 09:48:27 UTC (51 KB)
[v2]
Tue, 2 Sep 2025 09:50:44 UTC (51 KB)
[v3]
Tue, 10 Nov 2026 15:23:14 UTC (48 KB)
来源：arXiv cs.AI · arxiv.org