Causality and explanations in databases
Abstract
With the surge in the availability of information, there is a great demand for tools that assist users in understanding their data. While today's exploration tools rely mostly on data visualization, users often want to go deeper and understand the underlying causes of a particular observation. This tutorial surveys research on causality and explanation for data-oriented applications. We will review and summarize the research thus far into causality and explanation in the database and AI communities, giving researchers a snapshot of the current state of the art on this topic, and propose a unified framework as well as directions for future research. We will cover both the theory of causality/explanation and some applications; we also discuss the connections with other topics in database research like provenance, deletion propagation, why-not queries, and OLAP techniques.
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Apparent discrimination depends on the operational profile of the system's use.
“Apparent discrimination depends on the operational profile of the system system's use [7, 58].”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [58]
Operational profiles describe the input distributions likely to be observed in the field.
“Operational profiles [58] describe the input distributions likely to be observed in the field.”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [58]
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References 4 · Cited by 73
- Authors
- Meliou, A., Roy, S., & Suciu, D.
- Affiliations
- University of Massachusetts Amherst (US)
- University of Washington (US)
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2014-08
- Volume
- 7
- Issue
- 13
- Pages
- 1715-1716
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W333550619
- Cited by
- 73
- References
- 4
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 8 · 2023: 2 · 2022: 6 · 2021: 5 · 2020: 12 · 2019: 6 · 2018: 8 · 2017: 9 · 2016: 5 · 2015: 5
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the VLDB Endowment (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causality, explanation, databases, data understanding, provenance, OLAP, causal inference, unified framework
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