This is where I spent the most time and probably lost some points early.
Start by clarifying the goal: to measure the causal impact of the new rule on high-risk transfers while minimizing spillover. Then discuss randomization units (user, recipient, transaction) and their trade-offs, and propose methods to handle spillover such as cluster randomization or network-based designs.
Pro tip: Emphasize that spillover can bias estimates and that the choice of randomization unit should align with the level at which the treatment is applied and the level at which outcomes are measured. Mention that in financial settings, user-level randomization is often preferred but may require adjustments for recipient overlap.
Clarify that the treatment is a rule that blocks high-risk transfers, and the primary outcome is the reduction in fraudulent transfers without harming legitimate ones. Identify the unit of analysis (e.g., user, transfer).
Consider randomizing at the user level (sender), recipient level, or transfer level. Discuss pros and cons: user-level avoids within-user contamination but may have spillover if recipients are shared; transfer-level may have contamination if same user has both treated and control transfers.
Analyze how treatment of one user can affect outcomes of others via shared recipients. For example, if a sender is blocked, the recipient might receive transfers from other senders, altering their risk profile. Quantify potential spillover using network analysis.
Options: cluster randomization (e.g., randomize by recipient clusters or communities), switchback designs, or using a network-based randomization like graph cluster randomization. Alternatively, use a user-level randomization with spillover-adjusted estimators.
Run power analysis considering spillover, monitor for interference during the experiment, and use techniques like A/B testing with holdouts or causal inference methods to estimate direct and spillover effects.
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The raw sample size calculation was fine, p0 of 0.0012 means you need a massive number of transfers to detect a 20% relative drop because the base rate is so small.
First, compute the required sample size per arm for a two-sample proportion Z-test using the baseline fraud rate, the target relative reduction (20%), and desired power and significance levels. Then, adjust this sample size for clustering by multiplying by the design effect, which is 1 + (cluster size - 1) * ICC. Finally, round up to ensure sufficient sample size.
Pro tip: Always clarify the baseline fraud rate, significance level (alpha), and power (1-beta) before calculating; these are often assumed but critical. Also, mention that the design effect increases sample size, and if ICC is high, consider alternative designs like stratification or matched clusters.
Determine the baseline fraud rate (p1), the relative reduction (20%), so the new rate p2 = p1 * (1 - 0.20). Also confirm significance level (typically 0.05) and power (typically 0.80).
Use the formula for two-sample proportion Z-test: n = (Z_{α/2} + Z_{β})^2 * (p1(1-p1) + p2(1-p2)) / (p1 - p2)^2, where Z_{α/2} and Z_{β} are the critical values for the chosen alpha and beta.
Compute the design effect (DE) using the formula: DE = 1 + (m - 1) * ICC, where m is the cluster size and ICC is the intracluster correlation coefficient.
Multiply the sample size from step 2 by the design effect to get the required sample size per arm for cluster randomized design. Round up to the next integer.
Verify that the sample size is feasible and discuss any assumptions (e.g., normality approximation, known ICC). Consider sensitivity analysis for ICC and cluster size.
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Net = (fraud prevented * loss per incident) minus (extra legit blocks * cost per block).
Start by defining the detectable effect from the experiment (e.g., a 5% reduction in fraud rate or a 2% increase in false positives) and then translate it into weekly net dollar impact by calculating fraud losses prevented minus the cost of incorrectly blocking legitimate transfers. Use average transaction value, fraud rate, false positive rate, and cost per false positive to build a simple model, and present the result as a range with assumptions clearly stated.
Pro tip: Always express the net impact as a confidence interval or range, not a single number, and explicitly state that the cost of false positives includes not just lost revenue but also potential customer lifetime value and support costs—this shows you understand the full business impact.
Clarify what metric changed in the experiment (e.g., fraud rate reduction, false positive rate increase) and its magnitude, ensuring it is statistically significant and practically meaningful.
Multiply the reduction in fraud rate by the total weekly transaction volume and average transaction value to get the gross fraud savings.
Multiply the increase in false positive rate by weekly transaction volume and the average cost per false positive (including lost revenue, customer service, and churn impact).
Subtract the cost of false positives from the fraud losses prevented to get the weekly net dollar impact, and consider sensitivity analysis around key assumptions.
Sanity-check the result against business context, express uncertainty (e.g., confidence intervals), and summarize the net impact clearly for stakeholders.
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Primary metric is fraud rate on transfers, denominator being total transfers in the arm.
Start by clarifying the experiment's goal and hypothesis, then define one primary metric that directly measures success, secondary metrics that provide context, and guardrail metrics to ensure no harm. Specify precise denominators for each metric and outline a segmentation plan to detect heterogeneous treatment effects.
Pro tip: Proactively mention that you would pre-register the metric definitions and segmentation plan before launching the experiment to avoid p-hacking and ensure statistical validity. Also, consider using a holdout group for long-term guardrail monitoring.
Restate the experiment's objective and the expected causal effect to ensure alignment. This guides the selection of appropriate metrics.
Choose a single metric that directly measures the experiment's success, such as conversion rate or average revenue per user. Clearly define the denominator (e.g., total users exposed, total sessions) and the numerator.
Select secondary metrics that provide additional insights into user behavior (e.g., click-through rate, engagement time). Choose guardrail metrics to monitor potential negative impacts (e.g., latency, error rate, customer support contacts).
Identify key user segments (e.g., new vs. existing users, device type, geography) and plan to analyze treatment effects within each segment. Use appropriate statistical methods to account for multiple comparisons.
Describe how you will analyze the metrics (e.g., hypothesis tests, confidence intervals) and the decision rules for shipping, iterating, or stopping the experiment based on primary and guardrail metrics.
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O'Brien-Fleming was my pick over Pocock because it's more conservative early in the ramp, which matters when you're blocking real transactions and a false positive costs you real money.
Start by framing the experiment as a high-stakes A/B test where early stopping must balance speed and risk, then outline a group sequential design with alpha-spending and a ramp plan that gradually increases traffic. Finally, describe a post-production monitoring system that tracks key metrics and uses statistical process control to detect drift.
Pro tip: Emphasize that stopping rules must be pre-registered and that drift monitoring should include both statistical and practical significance, with clear escalation paths. Mention that you'd simulate the design under various scenarios to validate operating characteristics before launch.
Identify primary and guardrail metrics, then choose an alpha-spending function (e.g., O'Brien-Fleming) to control type I error across interim looks. Specify the number and timing of interim analyses based on expected traffic and effect size.
Propose a phased rollout: start with a small percentage of traffic (e.g., 5%) to catch technical issues, then increase in stages (e.g., 10%, 25%, 50%, 100%) based on predefined criteria like no guardrail violations and stable metrics.
Define both efficacy and futility stopping rules: stop for efficacy if the primary metric crosses the upper boundary, stop for futility if it crosses the lower boundary or if conditional power falls below a threshold. Include rules for safety or guardrail breaches.
After full rollout, set up ongoing monitoring using control charts (e.g., CUSUM) on key metrics, compare against experiment results, and track segment-level performance. Use sequential testing or Bayesian methods to detect gradual changes.
Define actions for when drift is detected: investigate root causes, consider rollback or adjustment, and communicate with stakeholders. Document learnings to improve future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.