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Activity Number: 82 - Evaluating Causal Effects Using Incomplete Data with Interference in Public Health Research
Type: Topic-Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #317100
Title: Causal Effects with Hidden Treatment Diffusion Over Partially Unobserved Networks
Author(s): Costanza Tortù* and Irene Crimaldi and Fabrizia Mealli and Laura Forastiere
Companies: Ecole Polytechnique and IMT Lucca and University of Florence and Yale University
Keywords: Causal Inference; Interference; Machine Learning; Treatment Diffusion; Complex Networks

In randomized experiments, interactions between units might generate a treatment diffusion process. For instance, if the intervention is an information campaign realized through a video, some treated units might share the treatment with their friends. Such a phenomenon causes a mis-allocation of individuals in the two treatment arms. This circumstance in turn might introduce a bias in the estimation of the causal effect of the intervention. Inspired by a 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 of missing ties. 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.

Authors who are presenting talks have a * after their name.

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