Phenomenological Causality
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Abstract (arXiv)
Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking 'what qualifies an action for being an intervention on the variable X' raises the question whether the action impacted all other variables only through X or directly, which implicitly refers to a causal model. To avoid this known circularity, we instead suggest a notion of 'phenomenological causality' whose basic concept is a set of elementary actions. Then the causal structure is defined such that elementary actions change only the causal mechanism at one node (e.g. one of the causal conditionals in the Markov factorization). This way, the Principle of Independent Mechanisms becomes the defining property of causal structure in domains where causality is a more abstract phenomenon rather than being an objective fact relying on hard-wired causal links between tangible objects. We describe this phenomenological approach to causality for toy and hypothetical real-world examples and argue that it is consistent with the causal Markov condition when the system under consideration interacts with other variables that control the elementary actions.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 15 Nov 2022.
Builds on
10 of 63 references.
References · 10
- 2021Towards Causal Representation Learningcited by 207
- 2017Causal Consistency of Structural Equation Modelscited by 114
- 2021Dive into Deep Learningcited by 111
- 2018Abstracting Causal Modelscited by 104
- 2014Visual Causal Feature Learningcited by 59
- 2014From Ordinary Differential Equations to Structural Causal Models: the deterministic casecited by 41
- 2018Causal Modeling of Dynamical Systemscited by 24
- 2022Causal Inference Through the Structural Causal Marginal Problemcited by 22
- 2020Amortized learning of neural causal representationscited by 14
- 2020Conditional independences and causal relations implied by sets of equationscited by 5
Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 1.
7 papers.
- 2023Causal models in string diagramscited by 14
- 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 12
- 2025How Causal Abstraction Underpins Computational Explanationcited by 10
- 2023Meaningful Causal Aggregation and Paradoxical Confoundingcited by 5
- 2023Reinterpreting causal discovery as the task of predicting unobserved joint statisticscited by 3
- 2024Systems with Switching Causal Relations: A Meta-Causal Perspectivecited by 2
- 2025Modeling Discrimination with Causal Abstractioncited by 2
- 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments6 Jul 2026
- 2025How Causal Abstraction Underpins Computational Explanation15 Aug 2025
- 2025Modeling Discrimination with Causal Abstraction14 Jan 2025
- 2024Systems with Switching Causal Relations: A Meta-Causal Perspective16 Oct 2024
- 2023Reinterpreting causal discovery as the task of predicting unobserved joint statistics11 May 2023
- 2023Meaningful Causal Aggregation and Paradoxical Confounding23 Apr 2023
- 2023Causal models in string diagrams15 Apr 2023