🤖 AI Summary
This paper addresses complex randomized experiments subject to interference between units—such as social network interventions—where standard causal inference assumptions fail.
Method: We develop a design-based theoretical framework for estimating treatment effects, introducing a family of design-compatible estimators and a scalar, interpretable measure of “experimental complexity.” We establish its theoretical connection to design variance, derive the asymptotic variance lower bound for unbiased estimation under arbitrary designs, and propose a consistent variance estimator.
Contributions/Results: Through interference modeling, design-based inference foundations, and network experiment simulations, we validate our approach on real-world social network data from an insurance adoption study. Our estimators achieve significantly improved estimation accuracy and consistent variance estimation compared to existing methods, providing a theoretically rigorous yet practically implementable analytical framework for complex experimental designs.
📝 Abstract
This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions.
