用异常事件证伪因果图
Falsifying Causal Graphs With Outlier Events
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中文总结 AI 辅助
研究如何在无真实因果关系时评估因果图，提出基于异常事件传播证伪候选因果图的方法，利用弱异常很少导致强异常原则，给出相关统计检验，有控制误报等功效，可单样本运行。
中文摘要 AI 辅助
真实的因果关系很少为人所知，从数据中推断因果图很困难。一个基本挑战是在没有地面真值的情况下，如何评估给定的因果图是否良好。我们提出基于候选因果图能否解释异常事件的传播来对其进行证伪。我们的方法利用一个关键原则：弱异常很少导致强异常。虽然该原则此前已用于根本原因分析以识别根本原因，但我们将其反过来用于证伪其隐含的异常传播与数据不一致的候选因果图。为此，我们针对候选图是真实因果图这一假设提出了首个统计检验，并表明它们具有误报控制、对错误因果图的功效保证，且可在单个异常样本下运行。
英文摘要
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.
发表机构
- University of Cambridge（剑桥大学）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- Amazon Research（亚马逊研究）
机构由 AI 辅助整理，请以论文原文为准。
