Cyclic Counterfactuals under Shift-Scale Interventions
Artificial Intelligence
2026-01-21 v2 Machine Learning
Statistics Theory
Machine Learning
Statistics Theory
Abstract
Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. biological systems) contain feedback loops or cyclic dependencies that violate acyclicity. In this work, we study counterfactual inference in cyclic SCMs under shift-scale interventions, i.e., soft, policy-style changes that rescale and/or shift a variable's mechanism.
Keywords
Cite
@article{arxiv.2510.25005,
title = {Cyclic Counterfactuals under Shift-Scale Interventions},
author = {Saptarshi Saha and Dhruv Vansraj Rathore and Utpal Garain},
journal= {arXiv preprint arXiv:2510.25005},
year = {2026}
}
Comments
Accepted at NeurIPS 2025