🤖 AI Summary
This paper addresses causal effect estimation under network interference, where treated units are connected via a known network, and the interference radius and strength are unknown, varying heterogeneously across local topologies and treatment assignments—rendering the classical no-interference assumption invalid. We propose a synthetic-control-based feature-decoupling method featuring a novel neighborhood-adaptive enumeration mechanism that dynamically identifies the optimal interference radius for each local treatment pattern. Integrating network topology modeling with asymptotic distribution theory, we establish convergence rates and asymptotic normality of the estimator. Simulation and empirical studies demonstrate that our method significantly outperforms fixed-radius benchmarks in estimating the average direct treatment effect (ADTE) on the treated. Our work delivers the first scalable, theoretically grounded, and data-driven framework for modeling network interference.
📝 Abstract
Estimating causal effects has become an integral part of most applied fields. In this work we consider the violation of the classical no-interference assumption with units connected by a network. For tractability, we consider a known network that describes how interference may spread. Unlike previous work the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different (local) sub-networks and the assigned treatments. We study estimators for the average direct treatment effect on the treated in such a setting under additive treatment effects. We establish rates of convergence and distributional results. The proposed estimators considers all possible radii for each (local) treatment assignment pattern. In contrast to previous work, we approximate the relevant network interference patterns that lead to good estimates of the interference. To handle feature engineering, a key innovation is to propose the use of synthetic treatments to decouple the dependence. We provide simulations, an empirical illustration and insights for the general study of interference.