Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning

TL;DR

This paper introduces a novel doubly robust neural network-based estimator for causal effects under networked interference, addressing model misspecification and achieving faster convergence.

Contribution

It adapts targeted learning to networked interference, establishing conditions for double robustness and designing an end-to-end estimator with theoretical guarantees.

Findings

The estimator achieves double robustness under specified conditions.

It converges faster than single nuisance models.

Experimental results validate effectiveness on real-world networks.

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 technique to the training of neural networks. Specifically, we generalize the targeted learning technique into the networked interference setting and establish the condition under which an estimator achieves double robustness. Based on the condition, we devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss. Moreover, we provide a theoretical analysis of our designed estimator, revealing a faster convergence rate compared to a single nuisance model. Extensive experimental results on two real-world networks with semisynthetic data demonstrate the effectiveness of our proposed estimators.