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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
This is a comparative evaluation of six counterfactual explanation methods for graph neural networks across synthetic and real-world datasets. The work identifies trade-offs between explanation size, coverage, and quality, but does not establish clinical utility or a definitive best method.
Comparative methods evaluation study. Intervention: Six state-of-the-art counterfactual explainer methods for graph neural networks. Compared with: Methods compared against each other using quantitative and qualitative metrics on diverse datasets.
Six state-of-the-art counterfactual explainers compared on diverse real-world and synthetic datasets Methods evaluated on both binary and multi-class graph and node classification tasks Existing methods exhibit different strengths and weaknesses, trading off between explanation size, coverage and quality
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A comparative evaluation study of existing explainability methods without novel intervention, clinical outcome, or definitive evidence—raises questions about method performance rather than answering clinical or translational questions.
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Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there is still a lack of general and efficient methods, especially when considering the quality of the generated explanations. Moreover, the problem remains far from solved, as existing methods exhibit different strengths and weaknesses, often trading off between explanation size, coverage and quality. For this reason, it is important to identify where each method performs well and where it falls short, so as to guide future research in the field. Thus, our study compares six state-of-the-art (SOTA) models on a diverse set of real-world and synthetic datasets, covering both binary and multi-class graph and node classification tasks, and evaluates their performance using diverse quantitative and qualitative metrics.
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