Causality-Based Scores Alignment in Explainable Data Management
📰 ArXiv cs.AI
Researchers investigate score alignment of attribution scores in explainable data management, focusing on causality-based methods
Action Steps
- Identify relevant attribution scores such as Causal Responsibility, Shapley Value, and Causal Effect
- Analyze the compatibility of these scores in different query and database scenarios
- Investigate the impact of score alignment on explainable data management
- Develop strategies to improve score alignment and overall system explainability
Who Needs to Know This
Data scientists and researchers on a team benefit from this work as it helps to understand the compatibility of different attribution scores, while product managers can use this knowledge to improve the explainability of their data management systems
Key Insight
💡 Different attribution scores may induce incompatible rankings of tuples, highlighting the need for score alignment in explainable data management
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📊 Causality-based scores alignment in explainable data management: a first investigation 📈
Key Takeaways
Researchers investigate score alignment of attribution scores in explainable data management, focusing on causality-based methods
Full Article
Title: Causality-Based Scores Alignment in Explainable Data Management
Abstract:
arXiv:2503.14469v5 Announce Type: replace-cross Abstract: Different attribution scores have been proposed to quantify the relevance of database tuples for query answering in databases; e.g. Causal Responsibility, the Shapley Value, the Banzhaf Power-Index, and the Causal Effect. They have been analyzed in isolation. This work is a first investigation of score alignment depending on the query and the database; i.e. on whether they induce compatible rankings of tuples. We concentrate mostly on cau
Abstract:
arXiv:2503.14469v5 Announce Type: replace-cross Abstract: Different attribution scores have been proposed to quantify the relevance of database tuples for query answering in databases; e.g. Causal Responsibility, the Shapley Value, the Banzhaf Power-Index, and the Causal Effect. They have been analyzed in isolation. This work is a first investigation of score alignment depending on the query and the database; i.e. on whether they induce compatible rankings of tuples. We concentrate mostly on cau
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