This is the kind of question where you can go broad fast and lose the thread.
Start by clarifying the goal and defining a clear, measurable hypothesis about how the new algorithm should improve ad recommendations. Then outline a rigorous A/B test design, including randomization, metrics, and statistical analysis, while addressing potential pitfalls like network effects and novelty bias.
Pro tip: Emphasize the importance of guardrail metrics to ensure the new algorithm doesn't harm user experience or other business metrics, and discuss how to handle interference in social networks, which is common at Meta.
Clearly state the hypothesis (e.g., new algorithm increases CTR) and identify primary, secondary, and guardrail metrics that align with business goals.
Choose an appropriate experimental design (e.g., A/B test, switchback, cluster randomization) considering user interference and network effects. Determine sample size, duration, and randomization unit.
Implement the experiment, ensure proper randomization, and monitor data quality and metric trends in real-time to detect issues early.
Perform statistical tests (e.g., t-test, bootstrap) to compare metrics between control and treatment, check for significance, and assess practical significance.
Based on results, decide whether to launch, iterate, or abandon the new algorithm. Consider long-term effects and potential follow-up experiments.
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I went with user-level randomization first, which is the obvious answer, but the follow-up was where it got interesting.
Start by clarifying the experiment's goal and constraints, then propose a randomization unit (e.g., user-level) and justify it based on the trade-off between bias and interference. Explicitly discuss the interference risks that arise from your choice and how you would mitigate them.
Pro tip: Mention that the choice of randomization unit often depends on the specific ads product and the nature of interference; for example, social ads may require cluster randomization to account for spillover, while search ads might be fine with user-level randomization.
Ask questions to understand the ads product, the target population, and the metrics of interest. This helps determine the appropriate randomization unit.
Choose a unit such as user, session, ad, or cluster (e.g., geographic region) and justify why it aligns with the experiment's goals and constraints.
Discuss potential interference: spillover effects (e.g., users in control seeing treatment ads), network effects, and competition for ad slots. Explain how these could bias results.
Suggest mitigation strategies such as cluster randomization, washout periods, or using intent-to-treat analysis. Discuss trade-offs like increased variance or reduced power.
Summarize your chosen unit and mitigation plan, emphasizing the balance between validity and practicality for the given scenario.
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Ran through a standard ramp: 1% then 5% then 50%, watching for SRM and guardrail violations at each stage.
Structure your answer around the experiment lifecycle: pre-launch planning (hypothesis, metrics, power analysis), launch execution (ramp plan, guardrails), and post-launch validation (sanity checks, statistical analysis). Emphasize how you balance speed with rigor, especially in a fast-paced environment like Meta.
Pro tip: Always mention guardrail metrics and pre-registration of your analysis plan—this shows you understand the risks of p-hacking and novelty effects, which are critical at scale.
Clearly state the null and alternative hypotheses, and identify the primary metric (OEC) plus secondary and guardrail metrics. Ensure metrics are aligned with the product goal and are sensitive to the change.
Calculate the required sample size using expected effect size, baseline metric, power (typically 80%), and significance level (typically 5%). Consider daily traffic and variance to estimate duration.
Plan a gradual ramp (e.g., 1% -> 5% -> 10% -> 50%) to catch bugs and guardrail violations early. Determine total duration based on sample size needs and business cycles (e.g., avoid holidays).
Run A/A tests or sample ratio mismatch (SRM) checks to validate randomization. Set up dashboards for real-time monitoring of guardrails and key metrics.
After the experiment ends, perform sanity checks (SRM, novelty effects), then analyze using appropriate statistical tests (e.g., t-test, CUPED). Interpret results with confidence intervals and practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Proposed geo holdouts and a phased ramp with sequential testing.
First, clarify why a 50/50 split isn't feasible (e.g., limited traffic, ethical concerns, network effects). Then, propose at least two alternative experimental designs, such as switchback tests or cluster-based randomization, and discuss their tradeoffs in terms of bias, variance, and generalizability. Conclude by emphasizing the importance of choosing the design that best balances validity and practical constraints.
Pro tip: Show that you understand the business context: sometimes a 50/50 split is impossible due to interference or user experience, so you might suggest a holdout group or a quasi-experimental method like difference-in-differences, but always acknowledge the limitations and how you'd validate results.
Ask or state why a 50/50 split isn't feasible—e.g., low traffic, ethical concerns, or network effects—to tailor your alternatives.
Suggest at least two viable approaches, such as switchback tests, cluster randomization, or quasi-experimental designs like synthetic control.
For each alternative, discuss tradeoffs: bias, variance, cost, time, and external validity compared to a 50/50 A/B test.
Choose the best approach given the constraints and explain how you'd mitigate its weaknesses (e.g., using covariates or sensitivity analysis).
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Acknowledge the constraint and propose a multi-pronged observational strategy: leverage historical data, quasi-experimental methods, and offline evaluation to assess the algorithm's impact. For cold-start users, suggest using contextual bandits or meta-learning with side information to make personalized recommendations without controlled experiments.
Pro tip: Emphasize the importance of defining a clear causal question and using domain knowledge to identify plausible confounders; this shows you understand the limitations and can still provide actionable insights.
Restate the objective: assess if the new algorithm improves key metrics without running controlled experiments. Confirm what data and tools are available.
Use methods like difference-in-differences, propensity score matching, or synthetic control to compare outcomes before and after deployment or across similar user groups.
Use logged data to simulate the new algorithm's performance via off-policy evaluation (e.g., inverse propensity scoring) and validate with holdout sets.
For new users, employ contextual bandits or meta-learning that leverage user demographics, device, and context to make recommendations without historical interactions.
Deploy gradually and monitor key metrics and guardrails; use sequential testing or Bayesian methods to detect issues early without formal experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.