The paper introduces sufficient explanations and a sufficiency-degree attribution score for database tuples in query answering, connects them to database repairs and causality explanations, and demonstrates computation via answer-set programs.
Causality-Based Scores Alignment in Explainable Data Management
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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 causality-based scores; and provide a syntactic dichotomy result for queries: on one side, pairs of scores are always aligned, on the other, they are not always aligned. It turns out that the presence of exogenous tuples makes a crucial difference in this regard.
fields
cs.DB 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Sufficient Explanations in Databases and their Connections to Database Repairs
The paper introduces sufficient explanations and a sufficiency-degree attribution score for database tuples in query answering, connects them to database repairs and causality explanations, and demonstrates computation via answer-set programs.