2 research outputs found
Policy design in experiments with unknown interference
This paper proposes an experimental design for estimation and inference on
welfare-maximizing policies in the presence of spillover effects. I consider a
setting where units are organized into a finite number of large clusters and
interact in unobserved ways within each cluster. As a first contribution, I
introduce a single-wave experiment to estimate the marginal effect of a change
in the treatment probabilities taking spillovers into account and test for
policy optimality. The design randomizes treatments independently within
clusters and induces local perturbations to treatment probabilities within
pairs of clusters. Using the estimated marginal effect, I construct a practical
test for whether a given treatment allocation rule maximizes welfare, and I
characterize its asymptotic properties. The idea is that researchers should
report estimates of the marginal effect and test for welfare-maximizing
policies: the marginal effect indicates the direction for a welfare
improvement, and the test provides evidence on whether it is worth conducting
additional experiments to estimate a welfare-improving treatment allocation. As
a second contribution, I design a multiple-wave experiment to estimate
treatment assignment rules and maximize welfare. I derive small-sample
guarantees on the difference between the maximum attainable welfare and the
welfare evaluated at the estimated policy (regret). A corollary of such
guarantees is that the regret converges to zero linearly in the number of
iterations and clusters. Simulations calibrated to existing experiments on
information diffusion and cash-transfer programs show that the method leads to
significant welfare improvements