# Causality-based Explanation of Classification Outcomes

arXiv: https://arxiv.org/abs/2003.06868 · HTML: https://mlsanity.com/paper/2003.06868/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 25 of 25)

- [Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals](https://mlsanity.com/paper/2103.11972/) (2021)
- [On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results](https://mlsanity.com/paper/2104.08015/) (2021)
- [Interpretable Data-Based Explanations for Fairness Debugging](https://mlsanity.com/paper/2112.09745/) (2021)
- [On the Tractability of SHAP Explanations under Markovian Distributions](https://mlsanity.com/paper/2405.02936/) (2024)
- [Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification](https://mlsanity.com/paper/2107.10159/) (2021)
- [XInsight: eXplainable Data Analysis Through The Lens of Causality](https://mlsanity.com/paper/2207.12718/) (2022)
- [SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism](https://mlsanity.com/paper/2510.21599/) (2025)
- [Causality from Bottom to Top: A Survey](https://mlsanity.com/paper/2403.11219/) (2024)
- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)
- [Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces](https://mlsanity.com/paper/2206.05257/) (2022)
- [Automated Discovery of Functional Actual Causes in Complex Environments](https://mlsanity.com/paper/2404.10883/) (2024)
- [Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation](https://mlsanity.com/paper/2303.06516/) (2023)
- [Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis](https://mlsanity.com/paper/2106.10562/) (2021)
- [Causal Explanations for Sequential Decision Making Under Uncertainty](https://mlsanity.com/paper/2205.15462/) (2022)
- [Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence](https://mlsanity.com/paper/2303.02829/) (2023)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)
- [A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI](https://mlsanity.com/paper/2412.14639/) (2024)
- [The Distributional Uncertainty of the SHAP score in Explainable Machine Learning](https://mlsanity.com/paper/2401.12731/) (2024)
- [The Causal-Effect Score in Data Management](https://mlsanity.com/paper/2502.02495/) (2025)
- [Feature Relevancy, Necessity and Usefulness: Complexity and Algorithms](https://mlsanity.com/paper/2505.09640/) (2025)
- [Reasoning about Counterfactuals and Explanations: Problems, Results and Directions](https://mlsanity.com/paper/2108.11004/) (2021)
- [Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection](https://mlsanity.com/paper/2202.12720/) (2022)
- [ExMo: Explainable AI Model using Inverse Frequency Decision Rules](https://mlsanity.com/paper/2205.10045/) (2022)
- [Answer-Set Programs for Repair Updates and Counterfactual Interventions](https://mlsanity.com/paper/2209.12110/) (2022)
- [Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability](https://mlsanity.com/paper/2610.01168/) (2026)

## Abstract (arXiv)

We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental evaluation with two real datasets from the financial domain.