Loading...
Mediation Analysis for Probabilities of Causation
Kawakami, Yuta Tian, Jin
Kawakami, Yuta
Tian, Jin
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an outcome, accounting for different causal pathways. We develop identification theorems for these new PoC measures, allowing for their estimation from observational data. We demonstrate the practical application of our results through an analysis of a real-world psychology dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Citation
Y. Kawakami and J. Tian, “Mediation Analysis for Probabilities of Causation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 25, pp. 26823–26832, Apr. 2025, doi: 10.1609/AAAI.V39I25.34886.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Keywords
Decisions makings, Identification theorems, Informed decision, Mediation analysis, Observational data, Real-world
Subjects
Source
39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Publisher
Association for the Advancement of Artificial Intelligence