Browse by author
Lookup NU author(s): Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 IEEE.The examination of complex diseases has greatly benefited from machine-learning techniques for interpreting biomedical signals. However, for diseases with multifactorial etiologies, such as Autism Spectrum Disorders (ASD), the inability of associative methods to distinguish between causal relationships and mere correlations often leads to unreliable outcomes. In contrast, causal models require well-defined causal structures, demanding a thorough understanding of their contributing factors. This study proposes a Generative Adversarial Network framework for Counterfactual Causal Inference (C2I-GAN) to uncover causal patterns from biomedical signals without a predefined causal structure. The framework leverages graph attention network to identify key features for directing counterfactual generation, applies generative adversarial learning to produce task-oriented counterfactuals, and supports inference by evaluating how modifications to specific features affect diagnostic outcomes. A case study of ASD evaluation is conducted on a resting-state EEG dataset (74 ASD and 143 TD children) using C2I-GAN against state-of-the-art methods (Associative: GAT; Counterfactual: CounteRGAN, Omnixai, and CXGAN). The findings show that C2I-GAN identified T3, T4, O1, and O2 channels as causal patterns while recognizing C3 and C4 as merely associative, aligning with the latest neuroscience evidence where counterpart methods failed. In terms of performance, the model improved actionability by 30% and accuracy by 10% compared to other counterfactual methods, and increased accuracy by 5% while reducing training loss by 20% against associative method, demonstrating enhanced precision and efficiency.
Author(s): Wang Y, Zuo Y, Chen D, Zomaya AY, Ranjan R, Chen J, Gao T
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Artificial Intelligence
Year: 2025
Pages: Epub ahead of print
Online publication date: 17/09/2025
Acceptance date: 02/04/2018
Date deposited: 07/10/2025
ISSN (electronic): 2691-4581
Publisher: IEEE
URL: https://doi.org/10.1109/TAI.2025.3610394
DOI: 10.1109/TAI.2025.3610394
Altmetrics provided by Altmetric
