When Randomization Is Not Enough: Auditing Exposure in Network Experiments

Randomized network experiments observe assignments and outcomes, but the physical exposure that defines a causal effect may be generated by an unknown mechanism. The same assignment–outcome law can then support exposure effects of opposite signs. We formulate active partial identification: bridge experiments measure physical exposure under selected assignments to narrow the union of candidate-implied causal targets. Under candidate completeness, outcome sufficiency, and calibrated invariant probes, candidate intervals and a structural e-process give a causal envelope valid throughout adaptive querying and stopping. Packing and feasible splits bound intrinsic audit complexity—the worst-case depth of a decision tree over causal targets—by causal-target geometry rather than by the number of mechanisms. For candidates with a common pre-audit law, heterogeneous-channel lower bounds separate search and confirmation costs; supplied ordered target cells with feasible cuts and a common binary-symmetric channel attain these scales up to universal constants. Experiments separate acquisition, calibration, and information-cost effects and quantify bridge sensitivity: target-aware auditing pays off when target and model geometries differ; public-data analysis illustrates the missing-anchor boundary left by unmeasured exposure.