Cyclic Counterfactuals under Shift-Scale Interventions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918293290352640 |
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| author | Saha, Saptarshi Rathore, Dhruv Vansraj Garain, Utpal |
| author_facet | Saha, Saptarshi Rathore, Dhruv Vansraj Garain, Utpal |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_25005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cyclic Counterfactuals under Shift-Scale Interventions Saha, Saptarshi Rathore, Dhruv Vansraj Garain, Utpal Artificial Intelligence Machine Learning Statistics Theory 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. |
| title | Cyclic Counterfactuals under Shift-Scale Interventions |
| topic | Artificial Intelligence Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2510.25005 |