Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference

TL;DR

This paper introduces a cascade-based randomization method for network experiments that leverages cascade seed nodes to better estimate causal effects under diffusion interference, outperforming existing methods.

Contribution

The paper proposes a novel cascade-based randomization design that uses cascade seed nodes to reduce bias in causal effect estimation under diffusion interference.

Findings

Outperforms state-of-the-art methods in real-world datasets

Reduces causal estimation bias in cascade propagation scenarios

Effective in both synthetic and real-world network experiments

Abstract

The presence of interference, where the outcome of an individual may depend on the treatment assignment and behavior of neighboring nodes, can lead to biased causal effect estimation. Current approaches to network experiment design focus on limiting interference through cluster-based randomization, in which clusters are identified using graph clustering, and cluster randomization dictates the node assignment to treatment and control. However, cluster-based randomization approaches perform poorly when interference propagates in cascades, whereby the response of individuals to treatment propagates to their multi-hop neighbors. When we have knowledge of the cascade seed nodes, we can leverage this interference structure to mitigate the resulting causal effect estimation bias. With this goal, we propose a cascade-based network experiment design that initiates treatment assignment from the cascade seed node and propagates the assignment to their multi-hop neighbors to limit interference during cascade growth and thereby reduce the overall causal effect estimation error. Our extensive experiments on real-world and synthetic datasets demonstrate that our proposed framework outperforms the existing state-of-the-art approaches in estimating causal effects in network data.