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

arXiv: https://arxiv.org/abs/2010.09911 · HTML: https://mlsanity.com/paper/2010.09911/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Learning Causal Effects on Hypergraphs](https://mlsanity.com/paper/2207.04049/) (2022)
- [Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2402.01845/) (2024)
- [Neighborhood Adaptive Estimators for Causal Inference under Network Interference](https://mlsanity.com/paper/2212.03683/) (2022)
- [Causal Lifting and Link Prediction](https://mlsanity.com/paper/2302.01198/) (2023)
- [Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence](https://mlsanity.com/paper/2305.17479/) (2023)
- [Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2503.07555/) (2025)
- [A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection](https://mlsanity.com/paper/2307.08237/) (2023)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects](https://mlsanity.com/paper/2503.01722/) (2025)
- [Data-Driven Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/2301.06615/) (2023)
- [Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms](https://mlsanity.com/paper/2302.05005/) (2023)
- [A/B testing under Interference with Partial Network Information](https://mlsanity.com/paper/2404.10547/) (2024)
- [Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation](https://mlsanity.com/paper/2604.15190/) (2026)
- [Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning](https://mlsanity.com/paper/2604.19186/) (2026)

## Abstract (arXiv)

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.