  In randomized experiments, interactions between units might generate a
treatment diffusion process. This is common when the treatment of interest is
an actual object or product that can be shared among peers (e.g., flyers,
booklets, videos). For instance, if the intervention of interest is an
information campaign realized through the distribution of a video to targeted
individuals, some of these treated individuals might share the video they
received with their friends. Such a phenomenon is usually unobserved, causing a
misallocation of individuals in the two treatment arms: some of the initially
untreated units might have actually received the treatment by diffusion.
Treatment misclassification can, in turn, introduce a bias in the estimation of
the causal effect. Inspired by a recent field experiment on the effect of
different types of school incentives aimed at encouraging students to attend
cultural events, we present a novel approach to deal with a hidden diffusion
process on observed or partially observed networks.Specifically, we develop a
simulation-based sensitivity analysis that assesses the robustness of the
estimates against the possible presence of a treatment diffusion. We simulate
several diffusion scenarios within a plausible range of sensitivity parameters
and we compare the treatment effect which is estimated in each scenario with
the one that is obtained while ignoring the diffusion process. Results suggest
that even a treatment diffusion parameter of small size may lead to a
significant bias in the estimation of the treatment effect.
