# The Causal-Effect Score in Data Management

arXiv: https://arxiv.org/abs/2502.02495 · HTML: https://mlsanity.com/paper/2502.02495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [Causality-based Explanation of Classification Outcomes](https://mlsanity.com/paper/2003.06868/) (2020)
- [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)
- [Uniform Reliability of Self-Join-Free Conjunctive Queries](https://mlsanity.com/paper/1908.07093/) (2019)
- [A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries](https://mlsanity.com/paper/2008.00896/) (2020)
- [A Unified Approach for Resilience and Causal Responsibility with Integer Linear Programming (ILP) and LP Relaxations](https://mlsanity.com/paper/2212.08898/) (2022)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)

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

- [Causality-Based Scores Alignment in Explainable Data Management](https://mlsanity.com/paper/2503.14469/) (2025)

## Abstract (arXiv)

The Causal Effect (CE) is a numerical measure of causal influence of variables on observed results. Despite being widely used in many areas, only preliminary attempts have been made to use CE as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score in the context of classical and probabilistic databases.