Cyclic Counterfactuals Under Shift–Scale Interventions

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.

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Text

Saha et al. "Cyclic Counterfactuals Under Shift–Scale Interventions." Advances in Neural Information Processing Systems, 2025.

Markdown

[Saha et al. "Cyclic Counterfactuals Under Shift–Scale Interventions." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/saha2025neurips-cyclic/)

BibTeX

@inproceedings{saha2025neurips-cyclic,
  title     = {{Cyclic Counterfactuals Under Shift–Scale Interventions}},
  author    = {Saha, Saptarshi and Rathore, Dhruv Vansraj and Garain, Utpal},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025},
  url       = {https://mlanthology.org/neurips/2025/saha2025neurips-cyclic/}
}