Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge

TL;DR

This paper introduces a method using staggered rollout designs to estimate causal effects in networked populations without requiring network knowledge, effectively handling interference under certain low-degree assumptions.

Contribution

It presents unbiased estimators for total treatment effect that do not depend on network structure, leveraging staggered rollout and polynomial extrapolation techniques.

Findings

Estimator performs well against baselines in simulations

Provides bounds on estimator variance

Works under low-degree interference constraints

Abstract

Randomized experiments are widely used to estimate causal effects across a variety of domains. However, classical causal inference approaches rely on critical independence assumptions that are violated by network interference, when the treatment of one individual influences the outcomes of others. All existing approaches require at least approximate knowledge of the network, which may be unavailable and costly to collect. We consider the task of estimating the total treatment effect (TTE), or the average difference between the outcomes when the whole population is treated versus when the whole population is untreated. By leveraging a staggered rollout design, in which treatment is incrementally given to random subsets of individuals, we derive unbiased estimators for TTE that do not rely on any prior structural knowledge of the network, as long as the network interference effects are constrained to low-degree interactions among neighbors of an individual. We derive bounds on the variance of the estimators, and we show in experiments that our estimator performs well against baselines on simulated data. Central to our theoretical contribution is a connection between staggered rollout observations and polynomial extrapolation.