Neighborhood Adaptive Estimators for Causal Inference under Network Interference
This work addresses causal inference for researchers in applied fields dealing with network data, but it is incremental as it builds on prior methods by introducing synthetic treatments to handle feature engineering.
The paper tackles the problem of estimating causal effects under network interference when the radius and intensity of interference are unknown and depend on local sub-networks and treatments, establishing rates of convergence and distributional results for estimators of the average direct treatment effect on the treated.
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.