Attribution Score Alignment in Explainable Data Management

• URL: http://arxiv.org/abs/2503.14469v2
• Date: Thu, 24 Apr 2025 22:13:50 GMT
• Title: Attribution Score Alignment in Explainable Data Management
• Authors: Felipe Azua, Leopoldo Bertossi,
• Abstract summary: We investigate the alignment of different scores on the basis of the queries at hand.<n>It turns out that the presence of Causal Responsibility makes a crucial difference in this regard.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different attribution-scores have been proposed to quantify the relevance of database tuples for a query answer from a database. Among them, we find Causal Responsibility, the Shapley Value, the Banzhaf Power-Index, and the Causal Effect. They have been analyzed in isolation, mainly in terms of computational properties. In this work, we start an investigation into the alignment of these scores on the basis of the queries at hand; that is, on whether they induce compatible rankings of tuples. We are able to identify vast classes of queries for which some pairs of scores are always aligned, and others for which they are not. It turns out that the presence of exogenous tuples makes a crucial difference in this regard.

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