arXiv:2510. 16703v3 Announce Type: replace-cross Abstract: The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 10664v1 Announce Type: new Abstract: The Relativity of Causal Knowledge (RCK) explains how a network of agents with different structural causal models can exchange causal knowledge through a shared interventionally consistent abstraction, or backbone.
arXiv:2606. 00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess.
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
arXiv:2608. 12657v1 Announce Type: new Abstract: Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification.
arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.
arXiv:2505. 15215v3 Announce Type: replace-cross Abstract: Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable.