mlsanityData to 14 Oct 2026

Causality-Aware Local Interpretable Model-Agnostic Explanations

arXiv 2212.05256 ↗First posted 10 Dec 2022cs.AI cs.LGcited by 0

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Abstract (arXiv)

A main drawback of eXplainable Artificial Intelligence (XAI) approaches is the feature independence assumption, hindering the study of potential variable dependencies. This leads to approximating black box behaviors by analyzing the effects on randomly generated feature values that may rarely occur in the original samples. This paper addresses this issue by integrating causal knowledge in an XAI method to enhance transparency and enable users to assess the quality of the generated explanations. Specifically, we propose a novel extension to a widely used local and model-agnostic explainer, which encodes explicit causal relationships within the data surrounding the instance being explained. Extensive experiments show that our approach overcomes the original method in terms of faithfully replicating the black-box model's mechanism and the consistency and reliability of the generated explanations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 10 Dec 2022.

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21 of 37 references.

References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  4. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  5. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  6. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  7. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 131
  8. 2019The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanationscited by 97
  9. 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
  10. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  11. 2020BayLIME: Bayesian Local Interpretable Model-Agnostic Explanationscited by 51
  12. 2019ALIME: Autoencoder Based Approach for Local Interpretabilitycited by 41
9 more
  1. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  2. 2020Black Box Explanation by Learning Image Exemplars in the Latent Feature Spacecited by 23
  3. 2020Post-hoc explanation of black-box classifiers using confident itemsetscited by 21
  4. 2018Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)cited by 19
  5. 2019Explaining Visual Models by Causal Attributioncited by 18
  6. 2019Do Not Trust Additive Explanationscited by 16
  7. 2020Improving LIME Robustness with Smarter Locality Samplingcited by 12
  8. 2020Ordered Counterfactual Explanation by Mixed-Integer Linear Optimizationcited by 11
  9. 2018Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projectionscited by 10

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