Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects

Abstract

In non-network settings, encouragement designs have been widely used to analyze causal effects of a treatment, policy, or intervention on an outcome of interest when randomizing the treatment was considered impractical or when compliance to treatment cannot be perfectly enforced. Unfortunately, such questions related to treatment compliance have received less attention in network settings and the most well-studied experimental design in networks, the two-stage randomization design, requires perfect compliance with treatment. The paper proposes a new experimental design called peer encouragement design to study network treatment effects when enforcing treatment randomization is not feasible. The key idea in peer encouragement design is the idea of personalized encouragement, which allows point-identification of familiar estimands in the encouragement design literature. The paper also defines new causal estimands, local average network effects, that can be identified under the new design and analyzes the effect of non-compliance behavior in randomized experiments on networks.

References in corpus (2)

• Engaging with Massive Online Courses
• Graph cluster randomization: network exposure to multiple universes

Cited by in corpus (7)

• Spillover Effects in the Presence of Unobserved Networks
• Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
• Spillover Effects in Cluster Randomized Trials with Noncompliance
• A systematic investigation of classical causal inference strategies under mis-specification due to network interference
• Experimental Design under Network Interference
• A Bayesian Analysis of Two-Stage Randomized Experiments in the Presence of Interference, Treatment Nonadherence, and Missing Outcomes
• Causal Inference with Noncompliance and Unknown Interference