Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
Finding all the causal stories that fit the data, not just one
When researchers try to map cause-and-effect relationships from data, they usually pick a single best explanation. This paper shows that multiple competing causal explanations can fit equally well—and that traditional optimization methods often miss this ambiguity, leading to false causal links. By sampling many plausible causal maps instead of hunting for one ideal one, the authors reveal which causal claims are truly supported by the data and which are artifacts of the search method.
Causal maps guide real decisions in medicine, policy, and engineering—from which treatments actually cause recovery to which factors drive climate change. If researchers unknowingly pick a causal story that fits the data but isn't the true one, their conclusions could be misleading. This method exposes when the data genuinely can't decide between competing causes, prompting researchers to either collect better data or acknowledge uncertainty rather than confidently act on false causal claims.