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In randomized experiments, interactions between units might generate atreatment diffusion process. This is common when the treatment of interest isan actual object or product that can be shared among peers (e.g., flyers,booklets, videos). For instance, if the intervention of interest is aninformation campaign realized through the distribution of a video to targetedindividuals, some of these treated individuals might share the video theyreceived with their friends. Such a phenomenon is usually unobserved, causing amisallocation of individuals in the two treatment arms: some of the initiallyuntreated units might have actually received the treatment by diffusion.Treatment misclassification can, in turn, introduce a bias in the estimation ofthe causal effect. Inspired by a recent field experiment on the effect ofdifferent types of school incentives aimed at encouraging students to attendcultural events, we present a novel approach to deal with a hidden diffusionprocess on observed or partially observed networks.Specifically, we develop asimulation-based sensitivity analysis that assesses the robustness of theestimates against the possible presence of a treatment diffusion. We simulateseveral diffusion scenarios within a plausible range of sensitivity parametersand we compare the treatment effect which is estimated in each scenario withthe one that is obtained while ignoring the diffusion process. Results suggestthat even a treatment diffusion parameter of small size may lead to asignificant bias in the estimation of the treatment effect.

Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks