Start by clarifying the schema and definitions (e.g., tables for requests, completions, riders, cities; how ETA is stored). Then outline a query plan: generate a date spine, join aggregated daily metrics per city, compute rolling medians and conversion rates, and apply filters for rider signup cutoff and UTC truncation. Finally, write the SQL with CTEs for readability and test edge cases.
Pro tip: Mention that rolling medians are computationally expensive and suggest using window functions with PERCENTILE_CONT or approximate methods if the dataset is large, and always validate the date spine against the actual data to avoid missing days.
Ask about table structures, definitions of 'shown ETA', 'request-to-completion conversion rate', and the cutoff date. Confirm whether the rolling median is per city or overall, and the exact date range.
Create a date spine covering the full range using GENERATE_DATE_ARRAY or a recursive CTE. Filter riders to those who signed up before the cutoff date, and truncate timestamps to UTC midnight for grouping.
Join requests and completions to the date spine, compute daily request counts, completion counts, and average shown ETA per city per day. Ensure days with zero requests are included via left join.
Use window functions to calculate the 7-day rolling median of shown ETA and the daily conversion rate (completions/requests) per city. Handle nulls and division by zero.
Assemble the query with CTEs, add comments, and consider performance (indexes, partitioning). Validate results by checking a few cities and dates manually.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The trip-level exposure thing tripped me up.
First, clarify the experiment design and define conversion at the trip level, ensuring the exposure label is assigned per trip. Then, aggregate conversion rates by city, treatment group, and period, and compute the difference-in-differences estimator using SQL. Finally, consider statistical significance and potential confounders.
Pro tip: Always check for balance in pre-period trends between treatment and control groups; if they differ, the DiD estimate may be biased. Also, consider clustering standard errors at the city level to account for within-city correlation.
Confirm the treatment assignment mechanism, the definition of conversion (e.g., ride completed, booking made), and the pre/post periods. Ensure exposure is at the trip level, not rider level.
Write a subquery to compute the conversion rate for each city, treatment group (treated/control), and period (pre/post). This involves counting conversions and total trips.
Use conditional aggregation or self-joins to calculate the DiD estimate: (treated_post - treated_pre) - (control_post - control_pre) for each city, then average across cities if needed.
Optionally, compute standard errors or confidence intervals, and check for pre-period parallel trends. Consider sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I know CUPED conceptually but writing the cluster-robust SE part from scratch in pseudocode is awkward.
Start by clearly defining the rider-level data structure and the CUPED adjustment formula, then walk through the pseudocode step by step, emphasizing the estimation of theta and the computation of adjusted outcomes. Finally, explain how to compute cluster-robust standard errors at the rider or city level to account for correlation within clusters.
Pro tip: When implementing CUPED, always validate that the pre-period covariate is balanced between treatment and control groups; if not, consider using a regression adjustment or stratification. Also, for cluster-robust standard errors, use the cluster-robust variance estimator (e.g., CR2) to avoid underestimating uncertainty when clusters are few.
Load rider-level data with treatment assignment, pre-period conversion (X), and post-period conversion (Y). Ensure X is measured before the experiment and is unaffected by treatment.
Compute theta as the sample covariance between Y and X divided by the sample variance of X. This can be done using numpy or pandas functions.
For each rider, calculate the CUPED-adjusted outcome: Y_adj = Y - theta * (X - mean(X)). This removes the variance explained by the pre-period covariate.
Calculate the difference in mean adjusted outcomes between treatment and control groups. This is the CUPED-adjusted uplift estimate.
Compute standard errors for the uplift using cluster-robust methods at the rider or city level. Use libraries like statsmodels or implement the formula manually, clustering by rider or city.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.