Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

TL;DR

This paper introduces a novel method using causal network motifs and a tree-based algorithm to better identify and estimate heterogeneous spillover effects in social network A/B tests, addressing limitations of existing approaches.

Contribution

It proposes a new approach that incorporates local network structures via motifs and treatment conditions, improving causal inference in networked experiments.

Findings

Method outperforms existing models in synthetic tests.

Effectively captures heterogeneity in spillover effects.

Applicable to large-scale real-world networks.

Abstract

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference for credible causal inference. Existing solutions to the network setting include accounting for the fraction or count of treated neighbors in a user's network, yet most current methods do not account for the local network structure beyond simply counting the number of neighbors. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the treatment assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ "causal network motifs", which are network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. Our approach can account for social network theories, such as structural diversity and echo chambers, and also can help specify network interference conditions that are suitable to each experiment. We test our method on a synthetic network setting and on a real-world experiment on a large-scale network, which highlight how accounting for local structures can better account for different interference patterns in networks.