Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning

arXiv:2405.03342v1 Announce Type: new
Abstract: Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one single nuisance function, may still encounter misspecification problems under networked interference without appropriate assumptions on the data generation process. To mitigate bias stemming from misspecification, we propose a novel doubly robust causal effect estimator under networked interference, by adapting the targeted learning …

abstract arxiv causal cs.lg function interference networks neural networks parametric robust space type via while