Regrets, Graphs, and the Price of Privacy: Federated Causal Discovery Grows Up
A hospital changes its treatment protocol. Another keeps the old one. A third removes an approval step that had quietly influenced several downstream decisions. Their datasets now disagree. The usual federated-learning instinct is to treat that disagreement as a problem: smooth it, average it, or design an aggregation rule robust enough to survive it. In causal discovery, however, some disagreements contain precisely the information the global model lacks. Removing a local dependency can expose a previously hidden causal pattern. A policy difference that looks like statistical inconvenience may function as an accidental experiment. ...