Experimentation on Endogenous Graphs

TL;DR

This paper addresses the challenge of estimating treatment effects in experiments where the network structure is endogenous and affected by the treatment, proposing new unbiased estimators that outperform existing methods.

Contribution

It introduces a class of unbiased, consistent, and asymptotically normal estimators for total treatment effects under endogenous network interference.

Findings

Proposed estimators are unbiased and outperform existing methods in simulations.

The approach applies to both bipartite and standard network experiments.

Simulation results demonstrate improved accuracy over conventional estimators.

Abstract

We study experimentation under endogenous network interference. Interference patterns are mediated by an endogenous graph, where edges can be formed or eliminated as a result of treatment. We show that conventional estimators are biased in these circumstances, and present a class of unbiased, consistent and asymptotically normal estimators of total treatment effects in the presence of such interference. We show via simulation that our estimator outperforms existing estimators in the literature. Our results apply both to bipartite experimentation, in which the units of analysis and measurement differ, and the standard network experimentation case, in which they are the same.