This is where I spent the most time and also where I fumbled first.
Start by framing the interference problem: in a two-sided marketplace, supplier ranking affects both riders and drivers, so user-level randomization will be biased by spillovers. Propose a cluster-based randomization (e.g., by city or driver cohort) with a switchback or staggered rollout design, and define clear metrics for order completion and marketplace balance.
Pro tip: Emphasize that you would run a power analysis accounting for intra-cluster correlation and consider a holdout group to measure long-term effects, showing you understand the trade-offs between bias and variance in networked experiments.
Map how the new ranking policy could affect both riders and drivers, and how interactions between them (e.g., driver repositioning, rider wait times) create spillovers across units.
Select a unit that minimizes contamination, such as geographic clusters (cities or neighborhoods) or driver cohorts, and consider switchback or staggered rollout to balance bias and power.
Specify primary metrics (e.g., order completion rate, ETA, match rate) and guardrails (e.g., driver utilization, rider cancellation) to capture both sides of the marketplace.
Account for clustering in power calculations, use appropriate statistical methods (e.g., cluster-robust standard errors, CUPED), and pre-register the analysis plan.
Run a pilot to check for spillovers, monitor for novelty effects, and be prepared to adjust the design if interference is stronger than expected.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the experiment's goal and the product surface (e.g., rider app, driver app, marketplace). Then propose a primary metric that directly measures the intended outcome, along with guardrail metrics that protect user experience and business health. Finally, specify the aggregation level (e.g., per rider, per driver, per city) and justify it based on the unit of randomization and the metric's sensitivity.
Pro tip: Always tie the choice of primary metric and guardrails to the experiment's hypothesis and the company's north star. For Lyft, consider marketplace dynamics: a change that boosts rider conversions might hurt driver utilization, so include both sides as guardrails.
Ask or infer what the experiment is trying to change (e.g., increase rider bookings, reduce driver wait times). This determines the primary metric.
Choose a single metric that directly measures the desired outcome and is sensitive to the change. For Lyft, examples: rides per rider, driver acceptance rate, or ETAs.
Identify metrics that should not degrade, such as rider cancellations, driver earnings, or system latency. Include both user experience and business health metrics.
Decide whether to aggregate at the user, driver, city, or trip level. This should align with the randomization unit and the metric's natural unit of analysis.
Explain why these metrics and aggregation levels are appropriate, and mention any trade-offs or potential pitfalls (e.g., network effects, seasonality).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I actually like power calculation questions because they're concrete.
Start by clarifying the test design (e.g., unit of randomization, metric type) and then walk through the standard sample size formula for a two-proportion z-test, plugging in the given values. After obtaining the base sample size, multiply by the design effect to account for clustering, and finally discuss any additional considerations like unequal allocation or multiple comparisons.
Pro tip: Always state the formula and assumptions explicitly, and mention that the design effect inflates the variance, so you multiply the sample size by the design effect (not the variance). Also, note that the calculation assumes equal allocation and no other adjustments; in practice, you'd round up and account for other factors like non-compliance.
Confirm the baseline rate (p1 = 0.60), minimum detectable effect (absolute +2 percentage points, so p2 = 0.62), two-sided alpha = 0.05, power = 0.80, and design effect = 1.2. State that you assume equal allocation and independent observations (before clustering adjustment).
Use the standard formula for comparing two proportions: n per group = ( (z_{1-α/2} + z_{1-β})^2 * (p1(1-p1) + p2(1-p2)) ) / (p2 - p1)^2. Plug in z_{0.975} ≈ 1.96, z_{0.80} ≈ 0.84, and the proportions to calculate n.
Multiply the base sample size per group by the design effect (1.2) to account for the increased variance due to clustering. This yields the required sample size per group under the clustered design.
Round up to the nearest integer, and mention that you might further adjust for unequal allocation, expected non-compliance, or multiple testing. Also, clarify that the total sample size is twice the per-group size if equal allocation.
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Two questions crammed into one, which I think was intentional.
Start by framing the problem as a need to balance overall treatment effect estimation with subgroup insights, using pre-registered heterogeneity analysis to avoid false positives. Then discuss sequential monitoring methods like alpha spending or group sequential designs to control Type I error while allowing early stopping. Emphasize practical implementation at Lyft, including logging, automation, and decision-making.
Pro tip: Pre-register your heterogeneity hypotheses and use a hierarchical model to borrow strength across subgroups, which reduces false positives and increases power. For sequential monitoring, use a validated alpha-spending function like O'Brien-Fleming and always adjust for multiple comparisons across subgroups.
Clearly specify the primary metric and pre-register which dimensions (city, time of day, supplier capacity quartile) you will analyze for heterogeneity, along with the expected direction of effects.
Use interaction terms in regression models or hierarchical Bayesian models to estimate subgroup effects while controlling for multiple comparisons. Consider causal forest for exploratory analysis.
Apply group sequential designs or alpha-spending functions (e.g., O'Brien-Fleming, Pocock) to allow interim analyses without inflating Type I error. Use software like gsDesign or sequential package.
Apply corrections like Bonferroni, Holm, or false discovery rate (FDR) for subgroup analyses, and ensure the sequential monitoring plan accounts for all interim looks.
Automate monitoring dashboards, define stopping rules, and communicate results with confidence intervals and effect sizes, emphasizing practical significance over statistical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely did not see this one coming in the way it was framed.
Start by acknowledging that marketplace rebalancing is a form of interference where treatment affects control units, violating SUTVA. Then outline a diagnostic approach to detect it, such as comparing pre-period trends and checking for negative treatment effects in control, and propose correction methods like cluster-based randomization, switchback designs, or modeling the interference explicitly.
Pro tip: In marketplace experiments, always monitor the ratio of treated to control units in each geographic area and look for spillover effects; if detected, consider using a difference-in-differences approach with synthetic controls to isolate the direct effect.
Compare pre-experiment trends between treatment and control geos, and check for unexpected changes in control metrics (e.g., a decline) that suggest treatment is drawing activity away. Use metrics like supply/demand balance, wait times, and conversion rates.
Measure the magnitude of spillover by analyzing cross-geo correlations or using a model that accounts for interference, such as a spatial regression or a marketplace simulation. Estimate the bias in treatment effect.
Select an appropriate design or analysis method: cluster randomization (e.g., by city), switchback experiments, or use of instrumental variables. Alternatively, model the interference directly with techniques like causal inference under interference.
After applying a correction, re-run diagnostics to ensure interference is mitigated. Compare results from different methods to check robustness, and if possible, run a holdout or validation experiment.
Clearly explain the detected interference, the chosen correction, and the adjusted effect size to stakeholders, highlighting any remaining uncertainty and recommendations for future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Saved the most operational question for last.
Start by defining the guardrail metrics and their thresholds, then outline a phased rollout with increasing traffic percentages and clear success criteria. Specify a fallback decision rule that triggers if any guardrail metric breaches for two consecutive days, including immediate actions like pausing the rollout and reverting to the previous version.
Pro tip: Emphasize the importance of pre-registering the analysis plan and guardrail thresholds to avoid p-hacking and ensure statistical validity. Also, mention the need for automated monitoring and alerting to detect breaches in real-time.
Identify key guardrail metrics (e.g., crash rate, cancellation rate, driver acceptance rate) and set acceptable thresholds based on historical data and business impact.
Plan a gradual rollout (e.g., 1%, 5%, 10%, 25%, 50%, 100%) with predefined durations and sample sizes to detect issues early while minimizing risk.
Set up real-time dashboards and automated alerts to track guardrail metrics daily and notify stakeholders of any breaches.
Specify that if any guardrail metric breaches its threshold for two consecutive days, the rollout will be paused, and the change will be reverted to the previous version.
Write a detailed rollout and fallback plan, share with cross-functional teams, and ensure alignment on roles and responsibilities during execution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.