Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta DS interview with a meaty experimentation question about network effects and cluster-based experiment design. The kind of question where you think you know the answer until they start asking follow-ups.

Questions Asked (1)

You're running an A/B test on a new recommendation algorithm and need to limit spillover between users. Walk through how you'd use clustered random sampling to assign test and control groups, what kinds of clusters make sense here, why this approach reduces interference, and what trade-offs it introduces.

A/B Testing & ExperimentationTechnical Trade-offs

Author's notes

I knew the high-level answer (use geographic markets or friend graph communities as clusters so treatment doesn't bleed across the boundary) but fumbled when pushed on the power implications.

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AI HintsAI Generated

Suggested Approach

Start by defining the interference problem and why user-level randomization fails for recommendation algorithms. Then explain how to form clusters (e.g., by social graph, geography, or time), randomize at the cluster level, and analyze with cluster-robust methods. Finally, discuss trade-offs like reduced power and increased variance.

Pro tip: Emphasize that cluster randomization is not a silver bullet: it reduces spillover but often requires larger sample sizes and careful cluster definition to avoid confounding. Mention that you'd validate cluster assignments by checking pre-experiment balance and interference metrics.

1. Define interference and clustering unit

Identify how interference occurs (e.g., social influence, shared content) and choose a clustering unit that captures the interaction, such as social communities, geographic regions, or time-based cohorts.

2. Assign clusters to test and control

Randomly assign entire clusters to treatment or control, ensuring each cluster is homogeneous and independent. Use stratified randomization if clusters vary in size or key metrics.

3. Analyze with cluster-level methods

Aggregate metrics at the cluster level or use mixed-effects models with cluster random effects to account for intra-cluster correlation. Compute cluster-robust standard errors.

4. Evaluate trade-offs and power

Discuss how clustering reduces effective sample size, increases variance, and may require longer experiments. Consider the bias-variance trade-off and potential for confounding if clusters are not well-defined.

5. Validate and monitor

Check for pre-experiment balance across clusters, monitor spillover metrics, and consider sensitivity analyses to ensure results are robust to cluster definition.

Key Points to Mention

Interference/spillover: users in control may be affected by treatment users through social ties or shared recommendations.
Cluster types: social graph communities (e.g., Facebook friends), geographic regions, temporal cohorts (e.g., time-based), or device-based clusters.
Randomization at cluster level: assign whole clusters to treatment/control to contain spillover within clusters.
Analysis adjustments: cluster-robust standard errors, mixed-effects models, or aggregated cluster-level analysis.
Trade-offs: reduced statistical power, increased variance, potential for confounding if clusters are not homogeneous, and longer experiment duration.
Practical considerations: cluster size and number, balance checks, and ensuring clusters are stable over time.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.