Why causal maps found by optimization can be misleading

Finding cause-and-effect relationships in data typically means optimizing a single best network structure. But this misses a crucial problem: different causal maps can fit the same data equally well. This work uses entropy-based inference to generate ensembles of plausible causal graphs rather than picking one winner, then shows that "optimized" networks often include spurious relationships absent from other equally valid structures.