Design-Based and Network Sampling-Based Uncertainties in Network Experiments

Kensuke Sakamoto and Yuya Shimizu

Papers from arXiv.org

Abstract: Ordinary least squares (OLS) estimators are widely used in network experiments to estimate spillover effects. We study the causal interpretation of, and inference for the OLS estimator under both design-based uncertainty from random treatment assignment and sampling-based uncertainty in network links. We show that correlations among regressors that capture the exposure to neighbors' treatments can induce contamination bias, preventing OLS from aggregating heterogeneous spillover effects for a clear causal interpretation. We derive the OLS estimator's asymptotic distribution and propose a network-robust variance estimator. Simulations and an empirical application demonstrate that contamination bias can be substantial, leading to inflated spillover estimates.

Date: 2025-06, Revised 2025-09
New Economics Papers: this item is included in nep-ecm, nep-exp and nep-net
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