The Causal-Effect Score in Data Management

Felipe Azúa, Leopoldo Bertossi
Proceedings of the Fourth Conference on Causal Learning and Reasoning, PMLR 275:874-893, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v275-azua25a, title = {The Causal-Effect Score in Data Management}, author = {Az\'{u}a, Felipe and Bertossi, Leopoldo}, booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning}, pages = {874--893}, year = {2025}, editor = {Huang, Biwei and Drton, Mathias}, volume = {275}, series = {Proceedings of Machine Learning Research}, month = {07--09 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v275/main/assets/azua25a/azua25a.pdf}, url = {https://proceedings.mlr.press/v275/azua25a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T The Causal-Effect Score in Data Management %A Felipe Azúa %A Leopoldo Bertossi %B Proceedings of the Fourth Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Biwei Huang %E Mathias Drton %F pmlr-v275-azua25a %I PMLR %P 874--893 %U https://proceedings.mlr.press/v275/azua25a.html %V 275 %X 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.
APA
Azúa, F. & Bertossi, L.. (2025). The Causal-Effect Score in Data Management. Proceedings of the Fourth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 275:874-893 Available from https://proceedings.mlr.press/v275/azua25a.html.

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