# Causality-Based Scores Alignment in Explainable Data Management

arXiv: https://arxiv.org/abs/2503.14469 · HTML: https://mlsanity.com/paper/2503.14469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 18 Mar 2025.

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

- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [The Shapley Value of Tuples in Query Answering](https://mlsanity.com/paper/1904.08679/) (2019)
- [From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back](https://mlsanity.com/paper/1412.4311/) (2014)
- [Computing the Shapley Value of Facts in Query Answering](https://mlsanity.com/paper/2112.08874/) (2021)
- [The Shapley Value in Database Management](https://mlsanity.com/paper/2401.06234/) (2024)
- [Quantifying Causal Effects on Query Answering in Databases](https://mlsanity.com/paper/1603.02705/) (2016)
- [Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints](https://mlsanity.com/paper/1611.01711/) (2016)
- [When is Shapley Value Computation a Matter of Counting?](https://mlsanity.com/paper/2312.14529/) (2023)
- [From Shapley Value to Model Counting and Back](https://mlsanity.com/paper/2306.14211/) (2023)
- [Banzhaf Values for Facts in Query Answering](https://mlsanity.com/paper/2308.05588/) (2023)
- [Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases](https://mlsanity.com/paper/2401.06493/) (2024)
- [From Database Repairs to Causality in Databases and Beyond](https://mlsanity.com/paper/2306.09374/) (2023)
- [A Unified Approach for Resilience and Causal Responsibility with Integer Linear Programming (ILP) and LP Relaxations](https://mlsanity.com/paper/2212.08898/) (2022)
- [The Causal-Effect Score in Data Management](https://mlsanity.com/paper/2502.02495/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sufficient Explanations in Databases and their Connections to Database Repairs](https://mlsanity.com/paper/2511.15623/) (2025)

## Abstract (arXiv)

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.