Falsifying Causal Graphs With Outlier Events
William Roy Orchard, Philipp M. Faller, Dominik Janzing
stat.ML, cs.LG
Submitted: 2026-07-13
Comments: Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026)
Code: https://github.com/worchard/rca-falsification
License: http://creativecommons.org/licenses/by/4.0/
The gist: True causal relationships are rarely known, and inferring causal graphs from data is hard.
Terminology
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.
Sources
- On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
- How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors
- From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
- Root Cause Analysis of Outliers with Missing Structural Knowledge
- Causal Discovery from Changes
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