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Activity Number: 592 - Evaluating Impact in Networks: Causal Inference with Interference
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #300289 Presentation
Title: Individualistic Effects in Randomized Trials Under Contagion
Author(s): Olga Morozova* and Daniel Eck and Forrest W Crawford
Companies: Yale School of Public Health and Yale School of Public Health and Yale School of Public Health
Keywords: contagion; interference; transmission model; vaccine trial; susceptibility effect; direct effect

The effects of interventions targeting potentially contagious outcomes are often evaluated in clusters of interacting individuals. Contagion induces complex dependence: an individual’s outcome may be influenced by other cluster members’ treatments, outcomes, or both. A widely accepted approach to evaluate the efficacy of such interventions defines the “direct effect” as the contrast between the potential infection outcomes under treatment and no treatment, averaged over the conditional distribution of possible treatment assignments to other individuals. Theoretical and empirical studies have shown that the direct effect may change depending on the treatment allocation within clusters. At the same time, the direct effect is often interpreted individualistically, as if it summarizes the infection risk of an individual under treatment versus no treatment, with exposure to infection held constant. Using an agent-based model of contagion, we explain this discrepancy, and show how some randomization designs ensure that the direct effect cannot have an individualistic interpretation.

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

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