Randomized Graph Cluster Randomization
Johan Ugander, Hao Yin

TL;DR
This paper introduces randomized graph cluster randomization (RGCR), a novel method that reduces variance in estimating the global average treatment effect in network interference settings by using a random clustering approach.
Contribution
The paper proposes RGCR, a new randomized clustering method that improves variance bounds and estimation accuracy over fixed graph cluster randomization (GCR).
Findings
RGCR achieves polynomial variance bounds, unlike exponential bounds for GCR.
Simulations show RGCR significantly reduces mean squared error in GATE estimation.
RGCR effectively mitigates luck-based variability in cluster assignments.
Abstract
The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz-Thompson (HT) and H\'ajek estimators of the GATE are unbiased and consistent, respectively, yet known to exhibit extreme variance under many designs and in many settings of interest. With a fixed clustering of the interference graph, graph cluster randomization (GCR) designs have been shown to greatly reduce variance compared to node-level random assignment, but even so the variance is still often prohibitively large. In this work we propose a randomized version of the GCR design, descriptively named randomized graph cluster randomization (RGCR), which uses a random clustering rather than a single fixed clustering. By considering an ensemble of many different cluster…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · SARS-CoV-2 detection and testing
