Adaptive Weighted Random Isolation (AWRI): a simple design to estimate causal effects under network interference

TL;DR

This paper introduces the Adaptive Weighted Random Isolation (AWRI) method, a simple yet effective approach for estimating causal effects in networked settings with interference, utilizing adaptive weights to reduce bias and improve accuracy.

Contribution

It proposes the AWRI design and RDIM estimator, extending causal inference techniques to directed networks with a novel weight selection algorithm for bias reduction.

Findings

Outperforms nine existing methods in simulations.

Effectively reduces bias through network-adaptive weighting.

Extends causal inference frameworks to directed networks.

Abstract

Recently, causal inference under interference has gained increasing attention in the literature. In this paper, we focus on randomized designs for estimating the total treatment effect (TTE), defined as the average difference in potential outcomes between fully treated and fully controlled groups. We propose a simple design called weighted random isolation (WRI) along with a restricted difference-in-means estimator (RDIM) for TTE estimation. Additionally, we derive a novel mean squared error surrogate for the RDIM estimator, supported by a network-adaptive weight selection algorithm. This can help us determine a fair weight for the WRI design, thereby effectively reducing the bias. Our method accommodates directed networks, extending previous frameworks. Extensive simulations demonstrate that the proposed method outperforms nine established methods across a wide range of scenarios.