I started with AUC out of habit and then had to walk it back, which was a bit embarrassing.
Start by framing the problem as a constrained optimization: maximize expected redemptions subject to a daily budget, using the model scores to rank users and a threshold to control spend. Then discuss how to evaluate the policy, emphasizing that offline metrics like AUC or accuracy are misleading because they ignore budget constraints and the top-K selection. Finally, propose a business-relevant success metric such as incremental redemptions per dollar spent, validated via A/B testing.
Pro tip: Emphasize that the decision policy must be evaluated on the top-ranked users, not the entire population, and that calibration of probabilities is crucial for accurate budget estimation. Mention that AUC is insensitive to the actual probability values and thus cannot inform budget allocation.
State the goal: maximize total expected redemptions per day subject to expected spend ≤ daily budget. Formulate as: choose a subset of users to target, where each user has a redemption probability p_i and a promo cost c_i, and the expected cost is sum(c_i * p_i) for targeted users.
Rank users by expected value per dollar (p_i * value / c_i) or by p_i if costs are uniform. Select the top users until the cumulative expected spend reaches the budget. This greedy approach is optimal for maximizing expected redemptions under a linear budget constraint when costs are uniform.
Use business metrics: incremental redemptions (or conversion rate) per dollar spent, total redemptions within budget, and return on ad spend (ROAS). For model evaluation, use precision@K or lift@K, which focus on the top-ranked users actually targeted.
AUC measures ranking across all thresholds and is insensitive to calibration; accuracy assumes a fixed threshold and balanced classes. Both ignore the budget constraint and the fact that only top users are targeted. A model with high AUC may still misallocate budget if probabilities are miscalibrated.
Run an A/B test comparing the new policy against the current one, measuring incremental redemptions and spend. Use holdout groups to estimate true lift and ensure the policy stays within budget without sacrificing long-term user value.
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Start by defining the target estimand (expected outcome under each policy) and the IPS estimator with its assumptions. Then extend to self-normalized IPS and doubly robust estimators, highlighting their bias-variance trade-offs and the role of cross-fitting to avoid overfitting. Finally, discuss practical considerations for comparing policies in an A/B testing context.
Pro tip: Emphasize that cross-fitting is crucial when using flexible models for propensity scores or outcome regression, as it prevents overfitting and ensures valid inference. Also, mention that self-normalized IPS is preferred when propensities are extreme or the sample size is small.
Clearly state the target: the expected outcome under each policy, i.e., V(π) = E[E[Y | X, A=π(X)]]. List the key assumptions: consistency, positivity (overlap), and no unmeasured confounding (ignorability).
Write the IPS estimator: V_IPS(π) = (1/n) Σ (I(A_i=π(X_i)) Y_i) / e(A_i|X_i). Then present the self-normalized version: V_SNIPS(π) = Σ w_i Y_i / Σ w_i, where w_i = I(A_i=π(X_i)) / e(A_i|X_i).
Introduce the outcome regression model μ(X, A) and write the DR estimator: V_DR(π) = (1/n) Σ [ μ(X_i, π(X_i)) + (I(A_i=π(X_i)) / e(A_i|X_i)) (Y_i - μ(X_i, A_i)) ]. Explain that it is unbiased if either the propensity model or the outcome model is correct.
Compare variances: IPS can have high variance with extreme propensities; SNIPS reduces variance but introduces bias; DR can be more efficient if outcome model is good. Explain cross-fitting: split data, fit models on one part, evaluate on the other, to avoid overfitting and enable valid inference.
Describe how to compare two policies: estimate V(π1) and V(π2) using the above estimators, compute the difference, and assess uncertainty via bootstrap or asymptotic variance. Highlight practical considerations like sample size and overlap.
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Calibration I knew well enough: isotonic regression for more data, Platt scaling when you have less.
Start by framing the problem as a constrained optimization under uncertainty: calibrate probabilities, then set thresholds to maximize expected profit subject to guardrails. Discuss methods for calibration (e.g., Platt scaling, isotonic regression) and how to adapt to distribution shift (e.g., online recalibration, importance weighting). Then explain how to set a daily threshold using budget constraints and optimize profit with guardrails via techniques like constrained optimization or Lagrangian methods.
Pro tip: Emphasize the need for continuous monitoring and feedback loops: calibration and thresholds must be updated regularly as distributions shift, and guardrails should be treated as hard constraints that may require trade-offs. Mention A/B testing to validate the approach in production.
Use held-out data to calibrate model outputs (e.g., Platt scaling, isotonic regression) and validate with reliability diagrams. Address distribution shift by re-calibrating on recent data or using domain adaptation techniques.
Given calibrated probabilities and cost/revenue per action, compute the expected profit for each user. Sort by expected profit and select top users until budget is exhausted, adjusting threshold dynamically based on daily budget and predicted volume.
Formulate as a constrained optimization: maximize expected profit subject to opt-out rate ≤ α and complaint rate ≤ β. Use Lagrangian relaxation or constrained Bayesian optimization to find the optimal threshold that balances profit and guardrails.
Implement online monitoring of calibration and guardrail metrics. If shift is detected, trigger re-calibration and re-optimization. Use A/B tests to validate changes and ensure guardrails are not violated.
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Got two easily: using features that encode prior coupon exposure, and including post-treatment variables in the feature set.
Start by clarifying the coupon targeting setup and the experiment design, then identify three distinct leakage risks that could arise from data collection, feature engineering, or deployment. For each risk, explain a concrete detection method (e.g., statistical tests, monitoring) and a prevention strategy (e.g., pipeline design, holdout sets).
Pro tip: Emphasize that leakage often stems from temporal or group contamination, and propose using a time-based split and user-level holdouts to simulate real-world conditions. Mention that you would validate with a pre-experiment A/A test to catch subtle leaks.
Ask clarifying questions about how coupons are targeted, what data is used, and how the experiment is run. Define data leakage as any information from the target or future that is inadvertently used in training.
List three specific risks: (1) temporal leakage from using future data, (2) group leakage from user overlap between train and test, and (3) target leakage from features derived from the coupon redemption itself.
For each risk, describe how to detect it: e.g., temporal leakage via time-based validation and checking feature importance of time-related features; group leakage via user-level split and checking for duplicate users; target leakage via correlation analysis and ablation tests.
For each risk, suggest prevention: use time-based splits and only past data; ensure user-level disjoint splits; exclude features that are only available after coupon issuance or redemption.
Tie the risks back to the importance of clean experimentation, and mention ongoing monitoring and validation to maintain trust in results.
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Shorter answer than I expected them to want.
Start by acknowledging that delayed redemption labels and per-user caps introduce temporal and selection biases that can distort model training and evaluation. Then outline a systematic approach: define the observation window, adjust labels for maturity, and incorporate cap constraints into the model or evaluation. Emphasize the importance of aligning training and evaluation with the business objective and using techniques like survival analysis or inverse propensity weighting to mitigate bias.
Pro tip: In practice, it's crucial to simulate the deployment environment during evaluation by applying the same cap logic and label delay as in production, and to monitor for feedback loops where the model's predictions influence future redemptions.
Establish a fixed time window after each user action to observe redemptions, and only include labels that have had sufficient time to mature. For immature labels, either exclude them or use techniques like survival analysis to handle censoring.
Incorporate the cap constraint into the training data by either truncating the number of redemptions per user or using a model that naturally accounts for caps (e.g., Poisson regression with an offset). During evaluation, apply the same cap logic to predictions to simulate real-world impact.
Use inverse propensity weighting or stratified sampling to correct for biases introduced by delayed labels and caps. Ensure that the training and evaluation sets are representative of the deployment population and time periods.
Simulate the effect of caps and delayed labels using historical data or A/B tests. Compare model performance under different assumptions to quantify bias and adjust accordingly.
Continuously monitor for drift in redemption patterns and cap effects. Set up alerts for when label delay distributions change, and retrain models periodically to adapt.
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I defaulted to a pretty standard answer here: monitor feature distributions over time, track prediction score distributions, set up shadow mode where M1 runs alongside M0 without affecting decisions, then canary by routing a small slice of traffic to M1.
Start by defining the two challenges—non-stationarity (distribution shifts over time) and cold-start users (new users with no history)—and how they affect model performance. Then propose a monitoring plan that tracks both data drift and performance metrics, with specific triggers for retraining or fallback. Finally, explain how shadow deployment and canarying can be used to safely test model updates and mitigate cold-start issues by gradually rolling out to new users.
Pro tip: Emphasize the importance of setting up automated alerts and a feedback loop: monitoring is only useful if it leads to action. Also, mention that cold-start users can be used as a natural experiment to detect non-stationarity early.
Identify key performance metrics (e.g., CTR, conversion) and data distribution metrics (e.g., feature drift, prediction drift) to monitor. Establish baselines from historical data and segment by user cohorts (new vs. existing).
Implement continuous monitoring for these metrics, with statistical process control or drift detection algorithms (e.g., PSI, KL divergence). Define thresholds for alerts and automate notifications to the team.
Deploy new models in shadow mode alongside the current production model, logging predictions without affecting user experience. Compare performance and drift metrics to detect issues before full rollout.
Gradually roll out the new model to a small percentage of users, ensuring representation of cold-start users. Monitor metrics closely and compare against control; if issues arise, roll back.
Use insights from monitoring and canary results to decide when to retrain models, adjust features, or update cold-start strategies (e.g., using contextual bandits or meta-learning).
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