Went with spam reply rate as the primary metric, which felt right, probability a recipient replies within 24 hours to a flagged message.
Start by defining the primary success metric that directly measures the algorithm's goal—reducing spam exposure—using a precise denominator and attribution window. Then, identify guardrail metrics that ensure the change doesn't harm user experience, engagement, or platform health, again specifying denominators and windows. Finally, explain how you would monitor these metrics in an A/B test to make a launch decision.
Pro tip: Emphasize that guardrails should be leading indicators of long-term harm, and consider using a holdout group to measure long-term effects. Also, be explicit about how you handle delayed delivery in metric calculations (e.g., using event time vs. processing time).
Choose a metric that directly captures the reduction in spam exposure, such as the rate of spam reports per 1,000 messages delivered to inbox. Specify the denominator (e.g., total messages delivered to inbox) and the attribution window (e.g., 24 hours after delivery).
Select metrics that ensure legitimate messages are not mistakenly delayed or routed to spam, such as the rate of false positives (legitimate messages sent to spam folder) per 1,000 legitimate messages, measured over a 7-day window.
Include metrics that capture potential negative user reactions, such as the rate of user complaints or support tickets related to delayed messages, per 1,000 active users, over a 14-day window.
Consider metrics that reflect overall ecosystem health, such as the rate of messages marked as spam by users (per 1,000 messages received) or the rate of users disabling the spam filter, measured over a 28-day window.
Describe how you would monitor these metrics in an A/B test, including statistical power, minimum detectable effect, and stopping rules. State that the primary metric must improve without any guardrail metric degrading beyond a pre-defined threshold.
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This is where I spent the most time and honestly the most interesting part.
Start by defining SUTVA and interference in the context of social experiments, then systematically compare each randomization unit (individual, conversation thread, recipient, geography) by identifying potential interference channels and when SUTVA is violated. For each, discuss trade-offs between bias and variance, and conclude with practical recommendations for choosing the unit based on the experiment's goals and constraints.
Pro tip: Emphasize that while cluster randomization reduces interference, it often increases variance and requires more clusters; always consider the trade-off and use techniques like cluster-robust standard errors or switchback designs when appropriate.
Explain that SUTVA requires no interference between units and a single version of treatment. Interference occurs when one unit's treatment affects another's outcome, violating SUTVA.
Discuss that randomizing at the user level can lead to interference through social interactions (e.g., sharing content, messaging). SUTVA breaks when treated users interact with control users, causing spillover effects.
Randomizing by conversation thread groups users who interact within a thread. Interference can occur if threads are connected (e.g., users in multiple threads), but SUTVA is more likely to hold within threads if interactions are contained.
For recipient-level, interference can happen if recipients interact with others (e.g., forwarding messages). For geography, interference can occur through cross-region interactions (e.g., travel, online communication). SUTVA breaks when spillovers cross cluster boundaries.
Weigh the trade-offs: finer randomization (individual) reduces variance but increases interference risk; coarser (geography) reduces interference but increases variance and reduces power. Recommend based on experiment goals and feasibility.
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ICC inflation, unequal cluster sizes, cluster drift, treatment leakage into new threads.
Start by defining cluster randomization and its purpose, then systematically outline the main pitfalls such as intra-cluster correlation, confounding, and implementation challenges. For each pitfall, propose concrete mitigation strategies tailored to Meta's large-scale experimentation context, emphasizing statistical rigor and practical trade-offs.
Pro tip: Demonstrate awareness of Meta's specific challenges, like network effects and cross-device user behavior, by suggesting advanced techniques such as graph cluster randomization or variance reduction methods. This shows you understand both theory and Meta's scale.
Briefly explain what cluster randomization is and why it's used (e.g., when individual randomization is infeasible due to interference or operational constraints).
List and explain the main pitfalls: increased variance due to intra-cluster correlation, confounding if clusters are not comparable, spillover/interference effects, and logistical complexity in implementation.
For each pitfall, suggest specific mitigations: use of mixed-effects models or GEE for correlation, stratification or matching to balance clusters, design-based approaches like graph cluster randomization to handle interference, and pilot studies to test logistics.
Discuss how these mitigations apply to Meta's products, such as social networks where interference is common, and mention tools like network analysis or switchback experiments as alternatives.
Summarize the trade-offs between cluster randomization and individual randomization, and recommend when cluster randomization is appropriate despite its pitfalls.
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DEFF formula is 1 + (m-1) * ICC so for threads: 1 + 2 * 0.07 = 1.14, for recipients: 1 + 19 * 0.02 = 1.38.
First, compute the design effect (DEFF) for each clustering level using the formula 1 + (m-1)*ICC, where m is the average cluster size. Then, adjust the effective sample size by dividing the total number of messages by the DEFF. Compare the effective sample sizes to determine which design yields more power, and consider practical implications such as implementation complexity and potential biases.
Pro tip: Don't just compare effective sample sizes; also consider that recipient-level clustering might introduce different biases or be harder to implement, so the recommendation should balance statistical efficiency with operational feasibility.
Use DEFF = 1 + (m-1)*ICC for thread-level (m=3, ICC=0.07) and recipient-level (m=20, ICC=0.02).
Divide the total number of messages (200M/day * 7 days = 1.4B) by the DEFF for each design to get the effective sample size.
Assess which design provides a larger effective sample size, indicating greater statistical power to detect the 10% relative reduction.
Weigh implementation complexity, potential biases, and business impact of each clustering approach before making a recommendation.
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Went with cluster-level difference-in-means with CR2 standard errors as primary, mentioned mixed-effects logistic as a robustness check.
Start by framing the analysis around the estimand (e.g., ITT vs. treatment-on-treated) and the experimental design, then walk through estimator choice, inference methods, variance reduction (CUPED), and handling of noncompliance and multiple exposures. Emphasize practical trade-offs and how you'd validate assumptions.
Pro tip: Meta often cares about scalability and practical impact, so mention how you'd automate the pipeline and monitor for metric movement over time. Also, be explicit about when CUPED might fail (e.g., if pre-period variance is low) and how you'd handle it.
Clarify the causal question: are you estimating the effect of assignment (ITT) or treatment received? Consider the randomization unit, exposure types, and potential interference.
For simple designs, use difference in means or regression adjustment; for noncompliance, consider IV/2SLS or CACE. Use robust standard errors or bootstrap for inference, and account for multiple testing if needed.
If pre-period data is available, use CUPED to reduce variance by adjusting for pre-experiment covariates. Validate that the pre-period metric is correlated with the outcome and not affected by treatment.
For noncompliance, use IV or CACE to estimate local average treatment effects. For multiple exposures, define exposure groups clearly and consider methods like exposure-based analysis or causal forests to estimate heterogeneous effects.
Check assumptions (e.g., exclusion restriction for IV), run sensitivity analyses, and consider alternative estimators to ensure robustness. Monitor for novelty effects and long-term trends.
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Named a threshold around the lower bound of the confidence interval clearing the MDE, guardrails not regressing, then a 5 to 25 to 100 percent ramp with a geo holdout throughout.
Start by defining a clear quantitative launch criterion that balances statistical significance, practical significance, and guardrail metrics. Then outline a phased ramp plan that includes monitoring, holdback groups, and decision gates to ensure safe and successful rollout.
Pro tip: Emphasize that the launch criterion should be pre-registered and include a minimum detectable effect that is practically meaningful, not just statistically significant. Also, mention that the ramp plan should have clear kill switches and success metrics at each stage.
Specify a quantitative threshold for launch, such as a statistically significant relative reduction in spam reply rate of at least 8% with guardrails not degraded beyond a pre-defined tolerance.
Ensure the experiment was adequately powered to detect the observed effect range (-8% to -12%) and that the confidence interval excludes zero and the minimum practical effect.
Confirm that all guardrail metrics (e.g., user engagement, revenue, latency) are within acceptable bounds and not statistically significantly worse than control.
Outline a phased rollout (e.g., 1%, 5%, 20%, 50%, 100%) with pre-defined monitoring periods and success criteria at each stage, including automatic rollback triggers.
During ramp, continuously monitor key metrics and guardrails; if any metric degrades, pause or rollback and investigate before proceeding.
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