Dataflow graphs as complete causal graphs

Paleyes, A.; Guo, Siyuan; Schölkopf, Bernhard; Lawrence, N. D.

doi:10.1109/CAIN58948.2023.00010

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Conference Paper

Dataflow graphs as complete causal graphs

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Guo, Siyuan
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf, Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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https://doi.org/10.1109/CAIN58948.2023.00010
(Publisher version)

https://arxiv.org/abs/2303.09552
(Preprint)

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Citation

Paleyes, A., Guo, S., Schölkopf, B., & Lawrence, N. D. (2023). Dataflow graphs as complete causal graphs. In 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN) (pp. 7-12). New York, NY: IEEE.

Cite as: https://hdl.handle.net/21.11116/0000-0010-5D60-A

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