Design-Based Inference under Random Potential Outcomes

Abstract

We study whether mechanism-level causal estimands, defined as expectations over latent stochastic environments, can be consistently recovered from a single realised randomised experiment. Identification alone does not guarantee recoverability. The target estimand averages over latent environments, whereas a single experiment provides only one realisation of such an environment. We show that suitably sparse local dependence induces an ergodic-type property under which cross-sectional averaging consistently recovers expectations over the latent outcome-generating mechanism. Under this structure, aggregate design-based estimators are consistent and asymptotically normal, and the variance becomes consistently estimable from a single experiment. Unlike classical finite-population inference, where Neyman-type variance estimators are structurally limited to conservative upper bounds, the proposed framework permits consistent variance estimation through the shift from fixed potential outcome schedules to stochastic mechanisms.

39 pages, 2 figures, 3 Tables, 2 Algorithms