mlsanityData to 7 Oct 2026

A Model of Causal Explanation on Neural Networks for Tabular Data

arXiv 2512.21746 ↗First posted 25 Dec 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

The problem of explaining the results produced by machine learning methods continues to attract attention. Neural network (NN) models, along with gradient boosting machines, are expected to be utilized even in tabular data with high prediction accuracy. This study addresses the related issues of pseudo-correlation, causality, and combinatorial reasons for tabular data in NN predictors. We propose a causal explanation method, CENNET, and a new explanation power index using entropy for the method. CENNET provides causal explanations for predictions by NNs and uses structural causal models (SCMs) effectively combined with the NNs although SCMs are usually not used as predictive models on their own in terms of predictive accuracy. We show that CEN-NET provides such explanations through comparative experiments with existing methods on both synthetic and quasi-real data in classification tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 25 Dec 2025.

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References · 27

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15 more
  1. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  2. 2017Distilling a Neural Network Into a Soft Decision Treecited by 230
  3. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 218
  4. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  5. 2016Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layerscited by 197
  6. 2019Feature relevance quantification in explainable AI: A causal problemcited by 164
  7. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
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  9. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
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  14. 2018Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activationscited by 23
  15. 2023Causal Analysis for Robust Interpretability of Neural Networkscited by 8

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