Toward Falsifying Causal Graphs Using a Permutation-Based Test

• URL: http://arxiv.org/abs/2305.09565v2
• Date: Thu, 19 Dec 2024 13:27:40 GMT
• Title: Toward Falsifying Causal Graphs Using a Permutation-Based Test
• Authors: Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing,
• Abstract summary: Existing metrics provide an $textitabsolute$ number of inconsistencies between the graph and the observed data.<n>We propose a novel consistency metric by constructing a baseline through node permutations.<n>By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric.
• Score: 11.826804773695033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an $\textit{absolute}$ number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a baseline through node permutations. By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric that captures whether the graph is significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true graph is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.

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