# Causal Identification of Sufficient, Contrastive and Complete Feature Sets in Image Classification

**Authors:** David A Kelly, Hana Chockler  
**arXiv:** [2507.23497](https://arxiv.org/abs/2507.23497)  
**Date:** 2025-07-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2507.23497v1/

## Key points

- The paper addresses limitations of existing algorithms for explaining image classifier outputs
- Proposes causal explanations as a more suitable alternative
- Introduces contrastive and complete causal explanations for image classifiers
- Demonstrates through experiments that different models exhibit varying sufficiency, contrastiveness, and completeness patterns
- Algorithms are efficiently computable and completely black-box, applicable to a wide range of models without requiring knowledge of internals or access to gradients
- Discusses related work in explainable artificial intelligence (XAI) and highlights advantages and limitations of formal and informal methods
- Contributes valuable insights into developing formal and rigorous explanations for image classifiers using causal reasoning
- Provides a comprehensive framework for partitioning pixels in an image into functional sets to reveal important information about how models make classifications

## Summary

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The paper "Causal Identification of Sufficient, Contrastive and Complete Feature Sets in Image Classification" addresses limitations of existing algorithms for explaining the outputs of image classifiers. The authors propose causal explanations as a more suitable alternative to overcome these limitations. They introduce contrastive causal explanations and complete causal explanations for image classifiers, demonstrating through experimental results that different models exhibit varying patterns of sufficiency, contrastiveness, and completeness. These algorithms are efficiently computable and completely black-box, making them applicable to a wide range of models without requiring knowledge of their internals or access to gradients. The paper also discusses related work in explainable artificial intelligence (XAI) and highlights the advantages and limitations of formal and informal methods. Overall, this paper contributes valuable insights into developing formal and rigorous explanations for image classifiers using causal reasoning. It provides a comprehensive framework for partitioning pixels in an image into functional sets to reveal important information about how models make classifications.