Implement Clustered Sampling to Mitigate Network Effects in Testing
Quick Overview
Evaluates experiment design for networked products using clustered randomization. Strong answers choose clusters that reduce spillovers, assign treatment at the cluster level, analyze intracluster correlation and power loss, and monitor contamination from cross-cluster interactions.
Implement Clustered Sampling to Mitigate Network Effects in Testing
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Technical Screen
##### Scenario
Planning an A/B test for a new recommendation algorithm while limiting network effects.
##### Question
Explain how you would apply clustered random sampling to pick test and control users. Which clusters would you choose, and why does this reduce interference? What trade-offs or risks does clustered sampling introduce?
##### Hints
Think markets, friend graphs, intracluster correlation, power loss.
Quick Answer: Evaluates experiment design for networked products using clustered randomization. Strong answers choose clusters that reduce spillovers, assign treatment at the cluster level, analyze intracluster correlation and power loss, and monitor contamination from cross-cluster interactions.
Implement Clustered Sampling to Mitigate Network Effects in Testing
You are planning an A/B test for a new recommendation algorithm in a networked product where users interact with friends, creators, groups, or local communities. Because one user's treatment can affect another user's outcome, individual randomization may create spillovers.
Constraints & Assumptions
Treat this as an experiment-design question for a networked product.
The goal is to estimate treatment effects while reducing interference between treatment and control.
Clusters should be operationally feasible and aligned with the product's real interaction graph.
Discuss the trade-off between less spillover and lower statistical power.
Clarifying Questions to Ask Guidance
What type of network creates spillovers: friends, followers, groups, creators, geography, language, or marketplace supply?
What metric is the experiment optimizing?
Are interactions mostly within markets or across the global graph?
How many clusters are available, and how balanced are they?
Part 1 - Construct Clusters
How would you construct clusters and assign treatment and control at the cluster level?
What This Part Should Cover Guidance
Define clusters from geography, language, social graph communities, groups, creator audiences, or interaction graph partitions.
Randomize clusters rather than individuals, with stratification by size, baseline metrics, market, or activity level.
Track exposure and cluster membership consistently.
Part 2 - Choose Cluster Types
Which clustering choices make sense in this context, and why do they reduce interference?
What This Part Should Cover Guidance
Geographic or language markets when interactions are mostly local.
Graph-based clusters when friend or creator interactions drive spillovers.
Trade-offs between implementation simplicity and true interaction containment.
Cross-cluster edge rates and spillover diagnostics.
Part 3 - Analyze Trade-offs
What risks or trade-offs does clustered sampling introduce?
Design effects, cluster-robust standard errors, randomization inference, and power calculations at the cluster level.
How to monitor and interpret spillovers.
What a Strong Answer Covers Guidance
A strong answer explains why individual randomization can fail under network effects, chooses clusters that internalize interactions, randomizes and analyzes at the cluster level, and quantifies the power and contamination trade-offs.
Follow-up Questions Guidance
How would you measure whether spillovers still occurred?
What if graph clusters are too large or imbalanced?
How would you explain the power loss to a product manager?