通过仅干预因果发现放松忠实性

中文摘要 AI 辅助

因果发现算法学习描述随机变量间因果依赖关系的网络。常见工作流程先是利用观测数据的条件独立属性确定部分有向因果关系，再通过干预确定未知因果方向。第一步的关键假设是忠实性：因果关联变量呈现统计依赖。许多自然系统有抵消路径以实现系统稳健性，这违反了忠实性，导致算法误删因果依赖。本文认为硬干预包含结构发现第一阶段被忽视的因果关联信息。我们表明一个允许抵消的温和假设——干预即时忠实性——足以用硬干预非参数识别因果结构。这些结果将干预定位为因果结构信息的主要载体，应优先于条件独立测试。为扭转范式，我们还在干预范围有限导致识别标准不满足时指定了等价类。

英文摘要

Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption -- called intervention-immediacy faithfulness -- that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventions as the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.

发表机构

• Thayer School of Engineering Dartmouth College（达特茅斯学院塞耶工程学院）
• Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）
• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。

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Accepted to UAI 2026