# Quantifying Causal Effects on Query Answering in Databases

arXiv: https://arxiv.org/abs/1603.02705 · HTML: https://mlsanity.com/paper/1603.02705/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2016.

## Built on by (most cited first, 10 of 10)

- [Shapley Value Computation in Ontology-Mediated Query Answering](https://mlsanity.com/paper/2407.20058/) (2024)
- [Shapley Revisited: Tractable Responsibility Measures for Query Answers](https://mlsanity.com/paper/2503.22358/) (2025)
- [From Database Repairs to Causality in Databases and Beyond](https://mlsanity.com/paper/2306.09374/) (2023)
- [Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis](https://mlsanity.com/paper/2106.10562/) (2021)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)
- [The Causal-Effect Score in Data Management](https://mlsanity.com/paper/2502.02495/) (2025)
- [Causality-Based Scores Alignment in Explainable Data Management](https://mlsanity.com/paper/2503.14469/) (2025)
- [Database Views as Explanations for Relational Deep Learning](https://mlsanity.com/paper/2509.09482/) (2025)
- [Sufficient Explanations in Databases and their Connections to Database Repairs](https://mlsanity.com/paper/2511.15623/) (2025)
- [Reasoning about Counterfactuals and Explanations: Problems, Results and Directions](https://mlsanity.com/paper/2108.11004/) (2021)

## Abstract (arXiv)

The notion of actual causation, as formalized by Halpern and Pearl, has been recently applied to relational databases, to characterize and compute actual causes for possibly unexpected answers to monotone queries. Causes take the form of database tuples, and can be ranked according to their causal responsibility, a numerical measure of their relevance as a cause to the query answer. In this work we revisit this notion, introducing and making a case for an alternative measure of causal contribution, that of causal effect. The measure generalizes actual causes, and can be applied beyond monotone queries. We show that causal effect provides intuitive and intended results.