Start by defining the goal and metrics, then discuss the randomization unit and spillover risks, and finally outline the experimental design and analysis plan. Emphasize trade-offs between user-level and cluster-level randomization, and how to measure both direct and indirect effects.
Pro tip: Consider using a cluster-randomized design with post-stratification or a switchback experiment to handle spillovers, and always pre-register your analysis plan to avoid p-hacking.
Clarify the primary goal (e.g., reducing bot comments) and select key metrics such as bot comment rate, user engagement, and false positive rate. Include guardrail metrics to monitor unintended consequences.
Decide between user-level, post-level, or cluster-level randomization. Discuss spillover risks: if bots interact across users, user-level randomization may contaminate control. Consider cluster randomization (e.g., by community or thread) to contain interference.
If spillovers are likely, use cluster randomization or a switchback design where treatment alternates over time. Ensure clusters are well-defined and balanced. Consider saturation or partial treatment designs to measure spillover effects.
Pre-specify analysis methods: intent-to-treat (ITT) for causal effect, and consider instrumental variables or difference-in-differences if non-compliance. Use cluster-robust standard errors. Measure direct and indirect effects via mediation or network analysis.
Explain why the chosen unit is appropriate given spillover potential, and validate assumptions (e.g., no interference within clusters). Discuss power analysis and sample size implications for cluster randomization.
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I went with human-visible comments per human DAU, creator retention, and report rates as the core metrics.
Start by clarifying the experiment's goal: to reduce bot activity while preserving genuine user experience. Then define primary success metrics that directly measure bot mitigation (e.g., reduction in bot traffic) and guardrail metrics that ensure no harm to real users (e.g., user engagement, false positive rate). Finally, discuss how you would monitor and iterate based on these metrics.
Pro tip: Emphasize the trade-off between aggressive bot blocking and user experience; propose a composite metric or a threshold-based approach to balance both. Also, mention the importance of segmenting metrics by user type (e.g., new vs. existing users) to detect unintended consequences.
Confirm the objective: reduce bot activity (e.g., spam, scraping, fake accounts) without harming legitimate user interactions. Identify the bot types and the affected surfaces (e.g., posts, messages, ads).
Choose metrics that directly quantify bot mitigation, such as reduction in bot-generated actions, decrease in spam reports, or increase in account verification rates. Ensure they are measurable and aligned with the goal.
Select metrics to monitor for unintended harm to real users, such as user engagement (DAU, time spent), false positive rate (legitimate users blocked), and system performance (latency). These ensure the mitigation doesn't degrade user experience.
Outline how you'll track metrics over time, including statistical power, duration, and segmentation (e.g., by user cohort, bot type). Plan for early stopping if guardrails are breached.
Explain how you'll interpret results: if primary metrics improve without guardrail degradation, roll out; if guardrails are harmed, iterate on the mitigation strategy. Highlight the importance of balancing both.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Excluding pre-flagged accounts felt obvious but I hadn't thought hard about high-risk geos until they asked.
Start by acknowledging the trade-off between internal validity and external validity when excluding flagged accounts and high-risk geographies. Then propose a principled eligibility framework that balances risk mitigation with generalizability, and finally describe variance reduction techniques that can recover precision lost from exclusions.
Pro tip: Always quantify the impact of exclusions on your sample size and power before finalizing criteria—stakeholders appreciate seeing the cost of risk mitigation. Also, consider running a parallel 'shadow' experiment on excluded segments to monitor for unintended effects.
Classify flagged accounts (e.g., fraud, policy violations) and high-risk geographies (e.g., regulatory, security) into tiers based on risk level and business impact. Clearly document why each tier is excluded or included, aligning with legal, security, and product teams.
Quantify how exclusions affect sample size, statistical power, and the representativeness of the remaining population. Determine if the experiment's conclusions can be extrapolated to excluded segments or if separate analyses are needed.
Select techniques such as CUPED (using pre-experiment covariates), stratification, or post-stratification to reduce variance and increase sensitivity, especially when sample size is reduced due to exclusions.
Put the eligibility criteria and variance reduction into practice, ensuring proper logging and monitoring. Set up guardrail metrics to detect any unintended consequences from exclusions.
Share the rationale, trade-offs, and results with stakeholders. Be prepared to adjust criteria based on learnings from the experiment or changes in risk landscape.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Day-level clustering inflates variance a lot compared to user-level, and I made sure to say that upfront.
Start by clarifying the metric definition and the unit of analysis (DAU vs. day-level clustering), then outline the standard power calculation steps: define the primary metric, specify the minimum detectable effect (MDE), estimate variance accounting for clustering, and compute required sample size and test duration. Finally, validate assumptions and discuss trade-offs (e.g., MDE, power, alpha) given the 14-day window and baseline of 5.5 comments per DAU.
Pro tip: Emphasize that with day-level clustering, the effective sample size is the number of days (or clusters), not the number of users, so you must adjust variance using the intra-cluster correlation (ICC) or design effect. Also, proactively mention that you would check for novelty effects and consider using a cluster-randomized design or CUPED to increase sensitivity.
Confirm that the primary metric is human-visible comments per DAU, and that randomization is at the day level (or user level with day-level clustering). Identify the unit of analysis for power: days or users, and note that clustering inflates variance.
Specify null and alternative hypotheses, choose alpha (typically 0.05) and power (typically 0.8), and determine the minimum detectable effect (MDE) based on business relevance (e.g., 1% relative lift).
Use historical data to estimate the variance of the metric at the appropriate unit. For day-level clustering, compute the intra-cluster correlation (ICC) and design effect (1 + (m-1)*ICC) where m is average cluster size, to adjust the variance.
Plug parameters into the power formula for the chosen design (e.g., two-sample t-test or cluster-randomized). Calculate required number of clusters (days) or users, then translate to test duration given daily traffic (DAU) and the 14-day window.
Check if the required sample size fits within the 14-day window; if not, discuss increasing MDE, extending duration, or using variance reduction techniques. Also consider multiple testing corrections and guardrail metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Novelty decay I'd seen before in content experiments so that part was fine.
Start by acknowledging that these are common validity threats in long-running online experiments and that each requires a tailored mitigation strategy. Then walk through a structured plan: diagnose the issue, apply statistical or design-based corrections, and validate that the experiment remains trustworthy. Emphasize that the goal is to preserve the causal interpretation of results despite dynamic user behavior.
Pro tip: Frame these threats as expected operational challenges rather than failures, and propose a pre-registered analysis plan that includes sensitivity checks for each. This shows you anticipate issues and design experiments defensively, which is highly valued at Meta.
Use diagnostic metrics (e.g., novelty checks, bot detection signals, treatment effect over time) to confirm whether novelty, bot migration, or adversarial adaptation is actually occurring. Quantify the magnitude and timing of the distortion.
Segment users into likely bots vs. humans, new vs. seasoned users, and early vs. late experiment periods. This allows you to estimate effects on the clean subpopulation and understand how the threat biases overall results.
Use methods like CUPED with pre-experiment covariates, time-weighted averages, or regression adjustment to control for novelty and bot contamination. For bot migration, consider intent-to-treat analysis or instrumental variables if bots are non-compliant.
If threats persist, propose design changes such as longer run times, holdout groups, or adversarial training for detection models. For adversarial adaptation, consider randomized rollout schedules or periodic re-randomization to break adaptation patterns.
Run sensitivity analyses to show results are stable under different assumptions. Clearly communicate the limitations and the steps taken to mitigate them, ensuring stakeholders understand the remaining uncertainty.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying why a proper A/B test isn't feasible (e.g., ethical, logistical, or network effects), then propose a specific quasi-experimental method like difference-in-differences, instrumental variables, or synthetic control that fits the context. Emphasize that you would validate assumptions through falsification tests, sensitivity analyses, and robustness checks, and discuss how you'd quantify uncertainty.
Pro tip: Show that you understand the trade-offs: quasi-experimental methods require stronger assumptions than A/B tests, so you'd prioritize methods with testable assumptions and always triangulate with multiple approaches. Mention that at Meta, you'd leverage large-scale observational data and consider techniques like switchback tests or geo-based experiments as intermediate solutions.
Ask why A/B testing isn't feasible—e.g., ethical concerns, spillover effects, or technical limitations—to determine which causal inference method is most appropriate.
Select a quasi-experimental design such as difference-in-differences, synthetic control, instrumental variables, or regression discontinuity, and explain why it fits the problem.
Explicitly list the key assumptions (e.g., parallel trends, exclusion restriction) and describe how you would test them using placebo tests, pre-trend checks, or overidentification tests.
Perform sensitivity analyses (e.g., varying model specifications, using different control groups) and robustness checks to assess how violations of assumptions would affect conclusions.
Use bootstrapping or Bayesian methods to quantify uncertainty, and if possible, triangulate findings with multiple methods or data sources to strengthen causal claims.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I structured this around on-experiment holdouts to isolate the effect, then stratified by new vs veteran users and creators vs passive consumers.
Start by validating the metric definition and data pipeline to rule out measurement artifacts, then segment false positives by user behavior, device, and demographics to identify patterns. Compare pre- and post-experiment false positive rates and use holdout groups to isolate the bot-mitigation system's impact.
Pro tip: Always check for Simpson's paradox: an overall rise in false positives might be driven by a shift in traffic mix (e.g., more new users) rather than the system itself. Also, consider that the bot-mitigation system might be working as intended but the experiment changed user behavior, leading to more false positives.
Ensure false positive rate is correctly defined and computed. Check for data quality issues, logging errors, or changes in labeling that could artificially inflate the rate.
Break down false positives by user demographics (age, gender, location), device type, and account age to see if specific groups are disproportionately affected.
Examine behavioral signals (e.g., session frequency, interaction patterns) of flagged users to distinguish real users from bots. Look for anomalies in the flagged population.
Use holdout groups and historical data to determine if the increase is due to the bot-mitigation system or external factors. Check if the system's thresholds or rules changed.
Review any changes to the bot-mitigation system during the experiment. Consider interactions with other experiment arms or features that might affect false positives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.