Falsifying Causal Graphs With Outlier Events
arXiv:2607.12145
The paper introduces statistical tests that use the propagation of outlier events to falsify candidate causal graphs, providing guarantees on false positives and detection power even with a single outlier sample.
Abstract
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.
Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026)