Intervening Before Selection: Counterfactual Effects in Adaptive Sparse Models

Adaptive sparse models select which components participate in a computation. A component's contribution to the selected computation can therefore differ from the model's dependence on its availability. We formalize this distinction through suppression after selection and exclusion before selection, which admits eligible alternatives. Across mixtures of experts, dynamic token selection, and fitted sparse semantic fields, the two interventions yield opposite effect signs and different maximizing components, even where rankings by representation change nearly coincide, and recomputing subsequent expert assignments can reverse their loss ordering. We derive a sufficient condition for invariance of the maximizing component across interventions and use suppression ranking margins to allocate additional measurements under a reference intervention. On inputs separate from threshold calibration, this allocation reduces disagreement with the reference maximizer relative to uniform random allocation at the same number of additionally measured groups, and motivates calibration objectives that also account for the effect gaps of the remaining errors.