Start by defining the randomization unit options (user, session, hashtag, etc.) and the trade-offs between them, emphasizing the need to align with the metric of interest and minimize interference. Then discuss how cross-session interference (e.g., user learning, network effects) can bias results and why user-level randomization is often preferred in such cases. Conclude with practical considerations like sample size, power, and implementation feasibility.
Pro tip: Mention that while user-level randomization is standard, if the recommender's effect is expected to be short-lived, session-level randomization might be acceptable—but always check for carryover effects. Also, consider using cluster randomization if interference is severe.
Determine what you're measuring (e.g., click-through rate, hashtag usage) and the natural unit for that metric (user, session, hashtag). This guides the randomization unit.
Evaluate how one user's exposure might affect another's (network effects) or how a user's past exposure affects future behavior (carryover). Cross-session interference is a key concern.
User-level randomization minimizes interference but may reduce power if effects are short-term. Session-level increases power but risks contamination. Consider cluster randomization if needed.
Check for interference via A/A tests or holdout groups. Calculate required sample size based on chosen unit and expected effect size.
During the experiment, monitor for unexpected interference or novelty effects. Be prepared to switch units or extend the test if issues arise.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through sticky bucketing to lock users into arms across sessions, and flagged that you might want a holdout on certain high-volume hashtags to avoid spillover through the hashtag graph itself.
Start by clarifying the experiment's goal, unit of randomization, and key metrics. Then outline a randomization strategy that ensures comparable groups, a phased rollout to monitor early signals, and specific measures to prevent contamination. Emphasize trade-offs and practical constraints at Meta's scale.
Pro tip: At Meta, experiments often involve social networks, so contamination is a major concern. Mentioning techniques like cluster randomization or ego-network isolation shows you understand the platform's unique challenges.
Clarify the hypothesis, primary metric, and the unit of randomization (e.g., user, session, cluster). Consider network effects and choose the appropriate unit to minimize interference.
Ensure groups are statistically equivalent via random assignment. For network experiments, consider cluster-based randomization or graph partitioning to reduce spillover.
Start with a small percentage (e.g., 1%) to detect early issues, then gradually increase while monitoring guardrail metrics. Use a ramp schedule that balances risk and speed.
Implement isolation techniques such as geo-based randomization, time-based separation, or network clustering. Monitor for spillover effects and adjust as needed.
Continuously check for sample ratio mismatch (SRM), contamination metrics, and guardrail metrics. Be prepared to pause or adjust the experiment if issues arise.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Primary metric was straightforward: 24-hour follow-through rate per exposed user.
Start by clarifying the experiment's goal: to increase user engagement with hashtags. Propose a primary success metric that directly measures this goal, such as hashtag click-through rate or usage. Then, identify guardrail metrics that ensure the change doesn't harm other key aspects like user retention, content quality, or system performance.
Pro tip: Choose guardrail metrics that are sensitive to potential negative side effects of the recommender, such as decreased diversity of hashtags or increased reports of spam. Also, consider both short-term and long-term guardrails to catch delayed effects.
Restate the purpose of the hashtag recommender: to help users discover and use relevant hashtags, thereby increasing engagement. Confirm with the interviewer if needed.
Select a metric that directly measures the desired outcome, such as the number of hashtag clicks per user or the percentage of posts that include recommended hashtags. Ensure it is aligned with the overall product objective.
Brainstorm possible negative consequences of the recommender, such as users feeling spammed, decreased content diversity, or technical issues like increased latency.
Choose at least three guardrail metrics that monitor these risks. Examples include user retention, hashtag diversity, report rate, and system performance metrics.
Explain why each metric was chosen and how it relates to the experiment. If needed, prioritize them based on potential impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the hardest part and I knew it going in.
Start by clarifying the metric and randomization unit, then compute the required sample size for a simple two-proportion z-test using the given baseline and relative MDE. Next, adjust for the intraclass correlation by inflating the variance using the design effect (1 + (m-1)*ICC), where m is the average number of sessions per user. Finally, present the final sample size in terms of users and discuss any additional considerations like multiple testing or novelty effects.
Pro tip: Always state your assumptions explicitly (e.g., average sessions per user, equal variance) and note that the ICC adjustment assumes a common correlation structure; in practice, you might use historical data to estimate m and ICC more precisely.
Confirm that the primary metric is a binary follow rate, randomization is at user level, and each user can have multiple sessions. Define the unit of analysis (user) and the unit of randomization (user).
Use the standard formula for two-proportion z-test: n = (Z_{α/2} + Z_β)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2, where p1=0.04, p2=0.04*1.05=0.042, α=0.05 (two-sided), β=0.20. Calculate n per arm.
Compute the design effect (DE) = 1 + (m - 1) * ICC, where m is the average number of sessions per user. Multiply the base sample size by DE to account for correlation among repeated sessions within users.
Present the adjusted sample size per arm and total. Mention that this assumes equal cluster sizes and a common ICC; if m varies, use the average or a more complex model. Also note any additional adjustments (e.g., for multiple comparisons, if needed).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran through pre-period covariate adjustment to reduce variance, flagging that you'd want at least a week of pre-period data per user.
Structure your answer around the four areas the question explicitly asks about, but frame them as a coherent bias-control strategy rather than a checklist. For each area, briefly state the technique, why it matters, and how you would implement it at Meta's scale. Emphasize trade-offs and practical constraints.
Pro tip: Show that you understand bias controls are not one-size-fits-all: tailor them to the experiment's specific risks (e.g., social network experiments need different interference handling than UI changes). Mention that you would pre-register the analysis plan and use holdouts to validate long-term effects.
Use stratification, covariate adjustment (CUPED), and paired designs to increase sensitivity without introducing bias. Explain how these reduce variance while maintaining valid inference.
Detect and mitigate novelty effects by monitoring treatment effect over time, using holdout groups, and analyzing early vs. late periods. Consider running longer experiments or using switchback designs.
Implement sequential testing with alpha spending (e.g., O'Brien-Fleming boundaries) or always-valid inference to allow early stopping without inflating false positives. Discuss the trade-off between speed and power.
For social or marketplace experiments, diagnose interference via cluster randomization, graph-based methods, or ego-network designs. Use diagnostics like spillover detection and consider cluster-level analysis.
Combine these controls into a pre-registered analysis plan, run A/A tests to validate, and use holdouts to measure long-term effects. Continuously monitor for bias and adjust as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.