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.
- Christoph Kern
- Unai Fischer-Abaigar
- Frauke Kreuter
