B1180
Title: Causal effects with hidden treatment diffusion over partially unobserved networks
Authors: Costanza Tortu - IMT School for Advanced Studies Lucca (Italy) [presenting]
Irene Crimaldi - IMT Lucca (Italy)
Fabrizia Mealli - University of Florence (Italy)
Laura Forastiere - Yale University (United States)
Abstract: In randomized experiments where some units are randomly assigned to a treatment, interactions between units might generate a treatment diffusion process. For instance, if the intervention of interest is an information campaign realized through a video or a flyer, some treated units might share the treatment with their friends. Such a phenomenon, which is usually hidden, causes a misallocation of individuals in the two treatment arms: some of the initially untreated units might have actually received the treatment by diffusion. This circumstance, in turn, might introduce a bias in the estimate of the causal effect of the intervention. 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, in the presence of a partially unknown network structure. We address the issue of a partially unobserved network by imputing the presence (or the absence) of missing ties, using random forests. Then, 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.
