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ReX: Causal discovery based on machine learning and explainability techniques
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DOI：10.1016/j.patcog.2025.112491.png)
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Abstract
En 中文
• A causal discovery method is introduced based on machine learning and explainability techniques. • Complex relationships in the data are captured and approximated by ML models, echoing their structural causal model. • Shapley values are used to help measuring how each feature contributes to data’s causal structure. • Results comparable to state-of-the-art methods in synthetic data and Sachs protein network support the approach. • A repository with open-source code is provided to foster wide adoption and further development.
Keywords:
Causal discovery
Explainability
Shapley values
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