English

Causality-Based Scores Alignment in Explainable Data Management

Databases 2026-04-07 v5 Artificial Intelligence

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.

Keywords

Cite

@article{arxiv.2503.14469,
  title  = {Causality-Based Scores Alignment in Explainable Data Management},
  author = {Felipe Azua and Leopoldo Bertossi},
  journal= {arXiv preprint arXiv:2503.14469},
  year   = {2026}
}

Comments

Extended version of published paper in with final revisions and appendix

R2 v1 2026-06-28T22:25:36.885Z