# Testing for arbitrary interference on experimentation platforms

arXiv: https://arxiv.org/abs/1704.01190 · HTML: https://mlsanity.com/paper/1704.01190/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 8 of 8)

- [Causal Message Passing for Experiments with Unknown and General Network Interference](https://mlsanity.com/paper/2311.08340/) (2023)
- [Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects](https://mlsanity.com/paper/2403.11332/) (2024)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Causal Inference with Complex Treatments: A Survey](https://mlsanity.com/paper/2407.14022/) (2024)
- [Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference](https://mlsanity.com/paper/2210.11355/) (2022)
- [Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference](https://mlsanity.com/paper/2405.12340/) (2024)
- [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)

## Abstract (arXiv)

Experimentation platforms are essential to modern large technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interference among users, under which the outcome of one user does not depend on the treatment assigned to other users, is rarely tenable on such platforms. Here, we introduce an experimental design strategy for testing whether this assumption holds. Our approach is in the spirit of the Durbin-Wu-Hausman test for endogeneity in econometrics, where multiple estimators return the same estimate if and only if the null hypothesis holds. The design that we introduce makes no assumptions on the interference model between units, nor on the network among the units, and has a sharp bound on the variance and an implied analytical bound on the type I error rate. We discuss how to apply the proposed design strategy to large experimentation platforms, and we illustrate it in the context of an experiment on the LinkedIn platform.