This part felt manageable until I got to unit of randomization.
Start by framing the business goal and articulating clear hypotheses about how the new recommendation algorithm will affect revenue. Then walk through the experimental design—randomization unit, sample size, and metrics—while emphasizing practical considerations like network effects and guardrail metrics. Conclude with how you'd analyze results and make a recommendation.
Pro tip: At DoorDash, randomization by user can be tricky due to shared accounts and device switching; consider randomizing by session or device ID, and always check for interference between treatment and control groups. Also, define revenue metrics that align with DoorDash's marketplace dynamics, such as orders per user and average order value.
State a clear null and alternative hypothesis: e.g., the new algorithm increases revenue per user compared to the current algorithm. Break down into primary and secondary hypotheses (e.g., impact on order frequency, basket size).
Decide whether to randomize by user, session, or store. Consider trade-offs: user-level avoids contamination but may dilute effect if algorithm is session-based; session-level increases power but risks spillover. At DoorDash, account for shared devices and multiple sessions per user.
Determine required sample size using power analysis: specify significance level (α=0.05), power (1-β=0.8), minimum detectable effect (MDE) based on business relevance, and baseline variance. Use historical data to estimate variance and consider clustering if randomizing by store.
Choose primary metric: revenue per user (or per session). Include secondary metrics: conversion rate, average order value, order frequency. Add guardrail metrics: customer satisfaction, delivery time, cancellation rate, and long-term retention.
Plan for statistical analysis: use t-tests or regression with covariates, check for novelty effects, and segment by user type. Consider sequential testing or Bayesian methods if peeking. Finally, assess practical significance and recommend rollout if results are positive and guardrails are intact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining that p=0.08 means the result is not statistically significant at the conventional 0.05 threshold, but it doesn't mean the feature has no effect. Then discuss the business context: the potential revenue lift, the cost of shipping, and the risk of false positives. Finally, recommend a decision based on a cost-benefit analysis and possibly suggest running the test longer or segmenting the data.
Pro tip: Mention that p-values are not the only factor; consider confidence intervals, practical significance, and the expected value of shipping. Also, at a company like DoorDash, emphasize the importance of aligning with business goals and avoiding premature decisions based on a single metric.
Explain that p=0.08 means there is an 8% chance of observing such an extreme result if the null hypothesis (no effect) were true. It does not prove the null; it's suggestive but not conclusive at the 0.05 level.
Look at the effect size and confidence interval. Even if not statistically significant, the lift might be practically meaningful. Consider the confidence interval: does it include zero? What is the range of plausible effects?
Evaluate the potential revenue impact, implementation costs, and risks. For DoorDash, consider factors like market competitiveness, user experience, and long-term effects. Also, check if the test was adequately powered.
Decide whether to ship, iterate, or run a follow-up test. If the expected value is positive and the risk is low, shipping might be justified. Otherwise, gather more data or refine the feature.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I was least prepared for.
Start by framing the core challenge of estimating causal impact without randomization, then compare the three methods on key dimensions: assumptions, data requirements, and applicability to DoorDash's marketplace. Use a concrete example (e.g., a new dispatch algorithm) to illustrate when each method is appropriate and how you would validate results.
Pro tip: Emphasize that no observational method is perfect; the best approach often combines multiple methods and triangulates results. Also, mention that in a marketplace like DoorDash, interference and dynamic pricing can violate key assumptions, so you must carefully check for SUTVA violations and consider cluster-level or switchback designs.
Define the treatment (e.g., new algorithm), outcome (e.g., delivery time), and population. Explain why randomization is infeasible (e.g., ethical, logistical, or marketplace interference).
For DiD: parallel trends; for PSM: conditional independence (no unmeasured confounders); for IV: relevance and exclusion restriction. Briefly describe how each estimates the causal effect.
Discuss data requirements (panel data for DiD, rich covariates for PSM, a valid instrument for IV) and threats to validity (e.g., time-varying confounders, poor overlap, weak instruments).
Give a concrete example: e.g., estimating the impact of a new dispatch algorithm on delivery time. Discuss which method might work best given available data and marketplace dynamics.
Suggest combining methods (e.g., DiD with PSM to adjust for covariates) and validating with placebo tests, sensitivity analyses, or if possible, a switchback experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.