When Does Causal Repair Ordering Survive Federated Evidence Formation?

Federated causal analysis can have enough data overall to answer a target question while no individual client can form the evidence needed for a particular test. The issue matters when the same target can be settled by several alternative sets of conditional-independence (CI) tests, which we call exact repairs. Federation can reverse this centralised preference: even reallocating the same pooled observations can make a shorter repair less likely to form than a longer one. The gap can be arbitrarily large: a target solvable by one query structurally and after pooled query-statistic aggregation can require arbitrarily many locally formed queries, with the gap persisting through any prescribed bounded aggregation depth. Formation ease and target-decision value can also rank repairs differently. Under random federation, we characterise how route support demands and federation scaling determine how much aggregation is needed, and give a concrete condition that preserves the centralised ordering. Experiments on public Bayesian networks show shallow recovery with limited aggregation, cases where a more expensive repair attains higher local readiness than every cheaper alternative, and strong sensitivity to how an unchanged pooled sample is split across clients.