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

TL;DR

This paper introduces a simulation-based sensitivity analysis method to evaluate how hidden treatment diffusion in networks can bias causal effect estimates in randomized experiments, especially when diffusion is unobserved or partially observed.

Contribution

It proposes a novel approach to assess the robustness of causal estimates against unobserved treatment diffusion using simulation scenarios.

Findings

Small diffusion parameters can cause significant bias in treatment effect estimates.

The method helps identify potential biases due to unobserved treatment sharing.

Diffusion can substantially alter causal inference results.

Abstract

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.