Implement Clustered Sampling to Mitigate Network Effects in Testing

Q: Implement Clustered Sampling to Mitigate Network Effects in Testing

This question evaluates a candidate's ability to design clustered randomization strategies to address interference and spillovers in networked experiments, testing competencies in causal inference, experimental design, and statistical reasoning within Analytics & Experimentation.

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Question

Scenario

You are planning an A/B test for a new recommendation algorithm in a networked product where users interact with friends and creators. Because user outcomes can be affected by others' assignments (network spillovers), standard individual randomization may violate the "no-interference" assumption.

Task

Design a clustered random sampling approach to select test and control users that limits interference.

Questions

How would you construct clusters and assign treatment/control at the cluster level?
Which clustering choices make sense in this context (e.g., markets, friend/interaction graphs), and why do they reduce interference?
What trade-offs or risks does clustered sampling introduce (e.g., intracluster correlation and power loss)?

Hints

Consider geographic/language markets and social/interaction graphs.
Aim to minimize cross-cluster ties that carry spillovers.
Intracluster correlation (ICC) inflates variance; anticipate power loss.
Think about analysis with cluster-robust methods and stratification.

Implement Clustered Sampling to Mitigate Network Effects in Testing

Scenario

Task

Questions

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Solution

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