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arXiv AI By Frederik Hytting J{\o}rgensen, Luigi Gresele, Sebastian Weichwald

What is causal about causal models and representations?

Read the original on arXiv AI →

The paper investigates why causal Bayesian networks are labeled as causal, noting that interventions are defined within the model while predictions refer to real-world actions. It introduces a formal framework that separates the causal model from the data-generating process and formalizes how actions map to interventions. The authors prove an impossibility result showing that no non‑circular interpretation can satisfy all desirable properties, and they explore the consequences for falsification, causal discovery, representation learning, and abstraction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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