Cyclic Counterfactuals under Shift-Scale Interventions
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Abstract (arXiv)
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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References · 13
- 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
- 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
- 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
- 2014From Ordinary Differential Equations to Structural Causal Models: the deterministic casecited by 41
- 2015backShift: Learning causal cyclic graphs from unknown shift interventionscited by 37
- 2022Causal Abstraction with Soft Interventionscited by 23
- 2024Benchmarking Counterfactual Image Generationcited by 22
- 2023Causal Modeling with Stationary Diffusionscited by 19
- 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Modelscited by 17
- 2021Matching a Desired Causal State via Shift Interventionscited by 16
- 2022Backtracking Counterfactualscited by 15
- 2024Counterfactual Generative Modeling with Variational Causal Inferencecited by 10
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