Dissecting Causal Mechanism Shifts via FANS: Function And Noise Separation
Gyeongdeok Seo, Jaeyoon Shim, Mingyu Kim, Hoyoon Byun, Yonghan Jung, Kyungwoo Song
Abstract
Identifying the drivers of causal mechanism shifts, distinguishing functional changes from noise alterations, known as dissection, is a critical yet under-explored problem in data science (e.g., biomedical science and manufacturing). This paper introduces a more general and unified framework, the function and noise separation framework (FANS), that detects and dissects shifts in non-additive, non-linear Structural Causal Models (SCMs) beyond existing additive noise models. Our approach is grounded in a theoretical independence criterion, where function shifts induce a statistical dependence between a node's parents and residual noise. Building on this foundation, we develop a practical two-stage algorithm to efficiently detect and dissect these shifts without retraining. Furthermore, we address the complex challenge of simultaneous function and noise shifts, introducing a formal assumption to resolve their inherent non-identifiability. Our results are corroborated by simulations. Our code is available at https://anonymous.4open.science/r/FANS-CFEB/.
BibTeX
@inproceedings{
seo2026dissecting,
title={Dissecting Causal Mechanism Shifts via {FANS}: Function And Noise Separation},
author={Gyeongdeok Seo and Jaeyoon Shim and Mingyu Kim and Hoyoon Byun and Yonghan Jung and Kyungwoo Song},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=blowNYT1qn}
}