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

2016/09/14 by Hyunseung Kang, Guido W. Imbens, Kang, Hyunseung +1 · 2 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1609.04464

openalex publication_date 2016/09/14 · openalex created_date 2016/09/30 · openalex updated_date 2026/07/28

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.

Citations

• Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology
• On causal inference in the presence of interference
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Graph cluster randomization: network exposure to multiple universes

Cited by

• Causal inference under interference: computational barriers and algorithmic solutions
• Policy-relevant causal effect estimation using instrumental variables with interference

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