# Testing for arbitrary interference on experimentation platforms

## TL;DR

This paper presents a new experimental design for testing the no-interference assumption in large-scale experimentation platforms, which is crucial for valid causal inference in networked environments.

## Contribution

It introduces a model-agnostic testing strategy inspired by econometric methods, with theoretical guarantees on variance and error rates, applicable to platforms like LinkedIn.

## Key findings

- The proposed test effectively detects interference in large experiments.
- The method has a sharp variance bound and controlled type I error.
- Application to LinkedIn demonstrates practical utility.

## Abstract

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.