This part felt deceptively clean but I kept second-guessing myself on the unit of analysis.
Start by clarifying the causal question: does listening to the 'Commute' playlist cause faster driving? Then define each component (population, unit, treatment, outcome) with precise operational definitions and time windows that align with the causal mechanism. Emphasize that the unit of analysis should be the trip (or driver-trip) to avoid aggregation bias, and specify time windows for treatment exposure and outcome measurement that capture immediate effects.
Pro tip: Mention that you would pre-register the analysis plan and consider using a within-driver design (e.g., switchback or randomized encouragement) to control for driver-level confounders, since randomizing playlist exposure per trip may be unethical or impractical.
Restate the hypothesis as a causal effect: does listening to the 'Commute' playlist during a trip cause an increase in driving speed? Define the target estimand (e.g., average treatment effect on the treated) and acknowledge potential confounders like time of day, route, and driver characteristics.
Population: all drivers who use the mobile app and have access to the 'Commute' playlist. Unit of analysis: the individual trip (or driver-trip), because treatment and outcome vary per trip. If interested in driver-level effects, use driver as unit but then treatment is a summary (e.g., proportion of trips with playlist).
Treatment: listening to the 'Commute' playlist during a trip. Control: not listening to the playlist (or listening to a neutral playlist). Define exposure precisely: e.g., playlist played for at least 50% of the trip duration, or any listening. Consider dosage (minutes listened) and timing (start, middle, end).
Outcome: driving speed, measured as average speed (mph) or proportion of time exceeding speed limit. Use GPS data from the app. Specify aggregation: per trip. Consider secondary outcomes like max speed, speed variance, or harsh braking events.
Treatment window: from trip start to trip end (or first 10 minutes if interested in immediate effect). Outcome window: same as treatment window for contemporaneous effect; for lagged effects, measure speed in the 5 minutes after playlist ends. Ensure windows align to avoid reverse causality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got five without too much trouble: time of day, road type, driver age, trip length, and weather.
Start by clarifying the study design and context, then systematically identify potential confounders by considering common sources like selection bias, measurement error, and external factors. For each confounder, propose a specific measurement method and explain how it would be incorporated into the analysis to adjust for its effect.
Pro tip: Demonstrate awareness that confounders can be addressed through design (e.g., randomization, blocking) or analysis (e.g., regression adjustment, stratification), and mention that in A/B testing, pre-experiment covariates and invariant metrics help detect and mitigate confounding.
Ask clarifying questions about the study design, population, treatment, and outcome to understand the potential for confounding. This ensures your answer is tailored to the specific scenario.
Brainstorm at least five major confounders by considering common categories: selection bias, temporal trends, user characteristics, external events, and measurement issues.
For each confounder, specify how it would be measured (e.g., using pre-experiment data, surveys, logs, or external data sources) and at what granularity.
Describe how you would adjust for each confounder in the analysis, such as stratification, regression, propensity score matching, or sensitivity analysis.
Discuss how you would validate the adjustments (e.g., checking covariate balance, placebo tests) and monitor for residual confounding during the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the 'lawyer is worried' framing really kicked in and I actually liked it as a constraint.
Start by framing the experiment as a safety-critical A/B test where the primary goal is to protect participants while measuring the causal effect of music on driving speed. Propose a randomized design with strict eligibility, real-time monitoring, and pre-registered stopping rules to ensure ethical and statistical validity.
Pro tip: Emphasize that safety guardrails and kill switches must be automated and independent of the experiment team to avoid bias, and that the primary metric should be a safety proxy (e.g., speed variance) rather than just average speed.
Specify inclusion/exclusion criteria (e.g., age, driving history, vehicle type) and randomize participants to treatment (playlist) or control (no music/podcast) using blocked randomization to balance covariates.
Implement real-time monitoring of speed and driving behavior with automatic alerts and a kill switch that stops the experiment if safety thresholds are breached (e.g., speed > 20% above limit).
Choose primary metric (e.g., mean speed or proportion of time speeding) and secondary metrics (e.g., speed variance, hard braking events). Use in-vehicle sensors or smartphone telematics for objective data.
Define stopping rules (e.g., O'Brien-Fleming alpha spending) for safety and efficacy, and specify the statistical test (e.g., ANCOVA) and power analysis to detect a clinically meaningful difference.
Obtain IRB approval, ensure informed consent, and plan for data privacy. Consider a run-in period and debrief participants, offering them the option to withdraw data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the hardest part and honestly I think I only did two of the four strategies justice.
Start by framing the problem: when randomization is infeasible, quasi-experimental methods can still provide credible causal estimates if their identifying assumptions hold. Then systematically walk through each method (within-driver FE, staggered DiD, IV, RD), explicitly stating the key assumption and at least one falsification test for each, and tie them to practical Google contexts like driver behavior or ranking thresholds.
Pro tip: Emphasize that no quasi-experimental method is a silver bullet—each requires strong assumptions and robustness checks. Show maturity by discussing how you'd triangulate multiple methods and quantify sensitivity to assumption violations, rather than claiming one method is definitive.
Acknowledge that when A/B tests aren't possible, we need quasi-experimental designs that mimic randomization. State that each method relies on untestable assumptions, so we must combine design with falsification tests.
Explain that this controls for time-invariant driver characteristics by comparing a driver to themselves over time. Key assumption: no time-varying confounders correlated with treatment. Falsification: test for pre-trends or placebo outcomes.
Describe using variation in treatment timing across units. Key assumption: parallel trends in absence of treatment. Falsification: event-study plots to check pre-trends, and Goodman-Bacon decomposition to assess bias from staggered timing.
Use an instrument that affects treatment but not the outcome except through treatment. Key assumptions: relevance (first stage) and exclusion restriction. Falsification: test for weak instruments (F-stat > 10), overidentification tests if multiple instruments, and placebo outcomes.
Exploit a cutoff in a ranking score that determines treatment. Key assumption: continuity of potential outcomes at the threshold. Falsification: McCrary density test for manipulation, covariate balance tests, and placebo cutoffs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mid-trip treatment was actually my best moment.
Acknowledge the complexities of mid-trip treatment start, partial compliance, and non-driver users, then propose a granular data model with trip-level and user-level flags to define treatment windows and filter populations. Emphasize the need for careful leakage prevention by aligning analysis units with the randomization unit and using time-aware features.
Pro tip: Frame the problem as an intent-to-treat (ITT) analysis with per-protocol (PP) as a secondary view, and explicitly discuss how you'd instrument the experiment to capture mid-trip starts and non-driver exposure.
Determine whether randomization occurs at the user, trip, or driver level, as this dictates the appropriate analysis unit and how to handle non-drivers and partial compliance.
For each user or trip, compute the exact time treatment began (e.g., mid-trip) and create a binary or time-varying treatment indicator that respects the start time to avoid contaminating pre-treatment periods.
Exclude or separately analyze users who are not drivers (e.g., riders) and account for partial compliance by using ITT and PP analyses, possibly with compliance-adjusted estimators.
Use trip-level or even event-level data with timestamps to ensure that features and outcomes are measured only after treatment start and that no future information leaks into the analysis.
Run robustness checks such as varying the exposure window, excluding edge cases, and comparing ITT vs. PP results to ensure conclusions are not driven by data artifacts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.