Decomposition of Probabilities of Causation with Two Mediators
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
Mediation analysis for probabilities of causation (PoC) provides a fundamental framework for evaluating the necessity and sufficiency of treatment in provoking an event through different causal pathways. One of the primary objectives of causal mediation analysis is to decompose the total effect into path-specific components. In this study, we investigate the path-specific probability of necessity and sufficiency (PNS) to decompose the total PNS into path-specific components along distinct causal pathways between treatment and outcome, incorporating two mediators. We define the pathspecific PNS for decomposition and provide an identification theorem. Furthermore, we conduct numerical experiments to assess the properties of the proposed estimators from finite samples and demonstrate their practical application using a realworld educational dataset.
Citation
Y. Kawakami and J. Tian, “Decomposition of Probabilities of Causation with Two Mediators,” in Proceedings of the Forty-First AAAI Conference on Artificial Intelligence, 2025, pp. 2044–2068.
Source
Proceedings of Machine Learning Research
Conference
UAI '25: Conference on Uncertainty in Artificial Intelligence
Keywords
Finite Samples, Identification Theorems, Mediation Analysis, Numerical Experiments, Primary Objective, Property, Real-world, Specific Component, Total Effect, Total Probabilities, Sampling
Subjects
Source
UAI '25: Conference on Uncertainty in Artificial Intelligence
Publisher
ML Research Press