# Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations

arXiv: https://arxiv.org/abs/2605.08688 · HTML: https://mlsanity.com/paper/2605.08688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 14 of 14)

- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results](https://mlsanity.com/paper/2104.08015/) (2021)
- [Logic for Explainable AI](https://mlsanity.com/paper/2305.05172/) (2023)
- [Declarative Approaches to Counterfactual Explanations for Classification](https://mlsanity.com/paper/2011.07423/) (2020)
- [From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back](https://mlsanity.com/paper/1412.4311/) (2014)
- [Complexity of Consistent Query Answering in Databases under Cardinality-Based and Incremental Repair Semantics (extended version)](https://mlsanity.com/paper/1605.07159/) (2016)
- [Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints](https://mlsanity.com/paper/1611.01711/) (2016)
- [Shapley Revisited: Tractable Responsibility Measures for Query Answers](https://mlsanity.com/paper/2503.22358/) (2025)
- [Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation](https://mlsanity.com/paper/2303.06516/) (2023)
- [From Database Repairs to Causality in Databases and Beyond](https://mlsanity.com/paper/2306.09374/) (2023)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)
- [Sufficient Explanations in Databases and their Connections to Database Repairs](https://mlsanity.com/paper/2511.15623/) (2025)
- [Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution](https://mlsanity.com/paper/2605.02640/) (2026)

## Abstract (arXiv)

We establish, from the point of view of Explainable AI (XAI), connections between Consistency-Based Diagnosis (CBD), on one side, and Actual Causality and Causal Responsibility, on the other. CBD has received little attention from the XAI community. Connections between these two areas could have a fruitful impact on XAI and Explainable Data Management.