Inference for Group Interaction Experiments

2026-07-02

AI Summary

This study addresses the failure of conventional causal inference methods in group interaction experiments, where within-group interactions and interference effects violate standard assumptions. The authors develop a design-based causal inference framework that systematically characterizes identifiability under various scenarios—such as fixed or random group assignment and presence or absence of interference—and proposes corresponding inference strategies. Innovatively, they introduce a coupling strategy to handle complex dependence structures, integrating sparse-sampling asymptotics, cluster-robust inference, and the potential outcomes framework. They demonstrate that, even under interference, cluster-robust methods consistently estimate marginalized exposure effects. Moreover, when interference is absent and assignment is randomized, the framework naturally reduces to the standard individual-level randomized experiment, thereby preserving compatibility with classical individual-level inference.

Abstract

A common experimental research design is one in which individuals are randomly allocated into groups that then interact under different group-level treatment conditions. We develop design-based inference for such "group interaction" experiments, covering scenarios in which groups are either fixed or randomly formed and in which potential outcomes are either fixed relative to others' group assignments or subject to interference. For each scenario, we characterize the causal estimand that the design targets and the inferential strategy appropriate to it. Working in a sparse-sampling asymptotic regime, we show that cluster-robust inference remains consistent and accounts for dependencies from various sources when interference is present, delivering valid inference on marginalized exposure effects. When interference is absent and groups are formed randomly, the design reduces to an individually randomized experiment, and individual-level heteroskedasticity-robust inference suffices for the average treatment effect. Our results on the asymptotic distribution of commonly used estimators rely on a novel coupling strategy that may be useful for design-based inference in other complex experiments.

Problem

• group interaction experiments
• causal inference
• interference
• randomization
• design-based inference

Innovation

• design-based inference
• group interaction experiments
• interference
• cluster-robust inference
• coupling strategy