用异常事件反推因果图真伪，无需真实图谱即可检验假设
Falsifying Causal Graphs With Outlier Events
- 基于弱异常不引发强异常的原理，反向验证候选因果图
- 仅需单个异常样本即可进行统计检验，控制误报率并保证检验效力
- 适合缺乏真实因果结构先验的工业故障诊断与系统分析场景
真实因果关系通常未知，从数据中推断因果图极具挑战。核心难题在于：在没有真实基准的情况下，如何评估一个候选因果图是否合理？本文提出一种基于异常事件传播来否定候选因果图的方法。其核心思想是：弱异常很少会引发强异常。该原则曾用于无图根因分析，我们反其道而行之，用它来检验候选图所隐含的异常传播是否与实际数据矛盾。为此，我们首次提出针对候选图是真实因果图这一假设的统计检验方法，证明其具有错误拒绝率控制、对错误图的检测能力，并可在仅有一个异常样本条件下运行。
原文摘要 · Abstract (English)
True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the absence of a ground truth. We propose falsifying candidate causal graphs based on whether they can explain the propagation of an outlier event. Our approach leverages a key principle: weak outliers rarely cause strong ones. While this principle has previously been used in root cause analysis to identify root causes without prior knowledge of the graph, we turn it on its head and use it to falsify candidate causal graphs whose implied outlier propagation is inconsistent with the data. To this end, we present the first statistical tests for the hypothesis that a candidate graph is the true causal graph, and show they have false positive control, power guarantees against incorrect causal graphs, and can operate with a single outlier sample.
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