Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

A Meta DS interview that went deep into causal inference and network experimentation, specifically around testing a misinformation warning label. The question was layered in a way I wasn't fully prepared for, covering everything from graph partitioning to adversarial detection.

Questions Asked (1)

You're testing a warning label shown before users reshare a suspected misinformation link. Since behavior spreads through a social graph, SUTVA is violated. Walk through how you'd design an experiment to measure total, direct, and spillover effects. Cover cluster construction, exposure mapping, outcome metrics, estimation strategy, power considerations, sensitivity analyses for contamination, and how you'd detect adversarial adaptation during the test.

A/B Testing & ExperimentationProduct Analytics & MetricsTechnical Trade-offs

Author's notes

This one hit me like five questions stacked inside one.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging SUTVA violation and framing the experiment as a cluster-randomized design with partial interference. Then walk through the design choices (clustering, exposure mapping, outcomes, estimation, power, sensitivity, adversarial detection) in a logical sequence, emphasizing trade-offs and practical constraints at Meta's scale.

Pro tip: Emphasize that spillover effects are often the most policy-relevant, and propose a two-stage randomization or saturation design to isolate them. Also, mention that adversarial adaptation can be monitored via changes in reshare patterns and content characteristics over time.

1. Cluster Construction and Randomization

Define clusters based on the social graph (e.g., communities, ego-networks) to minimize interference between units. Randomize treatment at the cluster level, ensuring balance on key covariates.

2. Exposure Mapping and Outcome Metrics

Specify how treatment exposure is defined for each user (e.g., direct exposure if they see the warning, indirect if connected to treated users). Choose outcomes like reshare rate, click-through, and misinformation spread.

3. Estimation Strategy for Total, Direct, and Spillover Effects

Use causal inference methods for partial interference (e.g., Horvitz-Thompson estimators, linear regression with cluster fixed effects) to decompose effects. Consider instrumental variables if compliance is imperfect.

4. Power Analysis and Sensitivity Checks

Conduct power analysis accounting for intra-cluster correlation and spillover. Perform sensitivity analyses to assess robustness to contamination and model misspecification.

5. Detecting Adversarial Adaptation

Monitor for changes in user behavior (e.g., increased resharing of unflagged content) and content characteristics over time. Use anomaly detection and compare treated vs. control clusters' adaptation patterns.

Key Points to Mention

SUTVA violation and partial interference framework
Cluster randomization and graph-based clustering methods (e.g., community detection)
Exposure mapping: direct vs. indirect exposure definitions
Estimators for spillover effects: Horvitz-Thompson, difference-in-differences with clusters
Power considerations: intra-cluster correlation, design effect, sample size inflation
Adversarial adaptation: monitoring reshare behavior, content mutation, and temporal trends

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