CID: Measuring Feature Importance Through Counterfactual Distributions

• URL: http://arxiv.org/abs/2511.15371v1
• Date: Wed, 19 Nov 2025 11:57:59 GMT
• Title: CID: Measuring Feature Importance Through Counterfactual Distributions
• Authors: Eddie Conti, Álvaro Parafita, Axel Brando,
• Abstract summary: This paper introduces a novel post-hoc local feature importance method called Counterfactual Importance Distribution (CID)<n>We generate two sets of positive and negative counterfactuals, model their distributions using Kernel Density Estimation, and rank features based on a distributional dissimilarity measure.<n>We showcase the effectiveness of our method by comparing with well-established local feature importance explainers.
• Score: 0.7559720049837458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous methods exist, the lack of a definitive ground truth for comparison highlights the need for alternative, well-founded measures. This paper introduces a novel post-hoc local feature importance method called Counterfactual Importance Distribution (CID). We generate two sets of positive and negative counterfactuals, model their distributions using Kernel Density Estimation, and rank features based on a distributional dissimilarity measure. This measure, grounded in a rigorous mathematical framework, satisfies key properties required to function as a valid metric. We showcase the effectiveness of our method by comparing with well-established local feature importance explainers. Our method not only offers complementary perspectives to existing approaches, but also improves performance on faithfulness metrics (both for comprehensiveness and sufficiency), resulting in more faithful explanations of the system. These results highlight its potential as a valuable tool for model analysis.

Related papers

• Fair Feature Importance Scores via Feature Occlusion and Permutation [41.73851747821022]
  We propose two model-agnostic approaches to measure fair feature importance.<n>First, we compare model fairness before and after permuting feature values.<n>Second, we evaluate the fairness of models trained with and without a given feature.
  arXiv  Detail & Related papers  (2026-02-09T21:02:52Z)
• ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design [0.815557531820863]
  We introduce a novel differentiable approach to global explainability by design.<n>We integrate feature importance estimation directly into model training.<n>Our approach yields globally faithful, stable feature rankings aligned with SHAP values and ground-truth feature importance.
  arXiv  Detail & Related papers  (2025-11-17T18:10:34Z)
• Reliable and Reproducible Demographic Inference for Fairness in Face Analysis [63.46525489354455]
  We propose a fully reproducible DAI pipeline that replaces conventional end-to-end training with a modular transfer learning approach.<n>We audit this pipeline across three dimensions: accuracy, fairness, and a newly introduced notion of robustness, defined via intra-identity consistency.<n>Our results show that the proposed method outperforms strong baselines, particularly on ethnicity, which is the more challenging attribute.
  arXiv  Detail & Related papers  (2025-10-23T12:22:02Z)
• MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics [72.00014675808228]
  Instability in Large Language Models evaluation process obscures true learning dynamics.<n>We introduce textbfMaP, a framework that integrates underlineMerging underlineand the underlinePass@k metric.<n>Experiments show that MaP yields significantly smoother performance curves, reduces inter-run variance, and ensures more consistent rankings.
  arXiv  Detail & Related papers  (2025-10-10T11:40:27Z)
• AICO: Feature Significance Tests for Supervised Learning [0.9474649136535703]
  AICO asks, for any trained regression or classification model, whether each feature genuinely improves model performance.<n>It does so by masking the feature's information and measuring the resulting change in performance.<n>AICO consistently pinpoints the variables that drive model behavior.
  arXiv  Detail & Related papers  (2025-06-29T21:15:40Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability [1.8274323268621635]
  Real Explainer (RealExp) is an interpretability method that decouples the Shapley Value into individual feature importance and feature correlation importance.<n>RealExp enhances interpretability by precisely quantifying both individual feature contributions and their interactions.
  arXiv  Detail & Related papers  (2024-12-02T10:50:50Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)
• Adversarial Infidelity Learning for Model Interpretation [43.37354056251584]
  We propose a Model-agnostic Effective Efficient Direct (MEED) IFS framework for model interpretation. Our framework mitigates concerns about sanity, shortcuts, model identifiability, and information transmission. Our AIL mechanism can help learn the desired conditional distribution between selected features and targets.
  arXiv  Detail & Related papers  (2020-06-09T16:27:17Z)
• Understanding Global Feature Contributions With Additive Importance Measures [14.50261153230204]
  We explore the perspective of defining feature importance through the predictive power associated with each feature. We introduce two notions of predictive power (model-based and universal) and formalize this approach with a framework of additive importance measures. We then propose SAGE, a model-agnostic method that quantifies predictive power while accounting for feature interactions.
  arXiv  Detail & Related papers  (2020-04-01T19:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.