Decision-making inherently involves cause–effect relationships that introduce causal challenges. We argue that reliable algorithms for decision-making need to build upon causal reasoning. Addressing these causal challenges requires explicit assumptions about the underlying causal structure to ensure identifiability and estimatability, which means that the computational methods must successfully align with decision-making objectives in real-world tasks.
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Acknowledgements
This work is supported by the DAAD program Konrad Zuse Schools of Excellence in Artificial Intelligence, sponsored by the Federal Ministry of Education and Research.
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Conceptualization and writing (original draft): C.K., U.F.-A., S.F.; writing (original draft): J.S., D.F.; supervision and writing (reviewing and editing): R.G., M.v.d.S., F.K.
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Nature Computational Science thanks Stefan Lessmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Kern, C., Fischer-Abaigar, U., Schweisthal, J. et al. Algorithms for reliable decision-making need causal reasoning. Nat Comput Sci 5, 356–360 (2025). https://doi.org/10.1038/s43588-025-00814-9
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DOI: https://doi.org/10.1038/s43588-025-00814-9
