Start by clarifying the grain (user-day) and the date range, then build a spine of all user-day combinations using a cross join or date spine to avoid missing days. Left join exposures, orders, and activity data to the spine, aggregating revenue and activity flags per user-day. Derive treatment, country launch status, and days since signup using window functions or joins, ensuring no duplicates via distinct or group by.
Pro tip: Always validate the panel by checking row counts against expected user-days and spot-checking edge cases like users who signed up mid-range or countries launched mid-range. This shows you understand data quality and experimental design.
Confirm the date range, definition of 'treated' (e.g., exposed to experiment), 'active' (e.g., logged in or made an order), and how to handle users who signed up after the start date. Establish that each row is a unique user-day.
Generate a complete set of user-day combinations for the date range. Use a cross join between users (filtered to those relevant) and a date series, or a recursive CTE, to ensure no missing days.
Left join exposures to flag treatment, orders to sum revenue per user-day, and activity logs to flag active. Use aggregation (SUM, MAX) and group by user-day to avoid duplicates.
Join a country launch date table to determine if the user's country had launched by that day. Compute days since signup as the date difference between the current day and the user's signup date.
Check for duplicates, ensure all user-days are present, and verify that metrics like revenue are correctly aggregated. Handle nulls appropriately (e.g., revenue as 0, active as false).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The lookahead bias angle is what makes this interesting.
Start by clarifying the definitions of 'active user' and 'revenue' and the date window, then outline the SQL or pandas logic to compute daily revenue and active users per country, followed by a 7-day rolling sum/division. Explicitly address how you handle users with no activity (e.g., exclude them from the denominator or treat as zero) and how you prevent lookahead bias by ensuring the rolling window only uses past data.
Pro tip: Mention that you would validate the rolling calculation by manually checking a few edge dates and that you'd use a window frame like ROWS BETWEEN 6 PRECEDING AND CURRENT ROW to avoid including future days.
Confirm what 'active user' means (e.g., users with at least one activity in the period) and how revenue is attributed (e.g., daily revenue per user). Also confirm the two countries and the exact date window.
For each country and date, calculate total revenue and count of active users. Ensure you only include data within the date window and handle missing dates appropriately.
Decide whether to exclude inactive users from the denominator (typical for 'active user' metric) or include them with zero revenue. Explain the impact on the metric and justify your choice.
Use a window function to compute the rolling sum of revenue and rolling sum of active users over the past 7 days (including current day), then divide. Ensure the window only looks backward to avoid lookahead bias.
Check for anomalies, ensure the rolling window is correctly aligned, and interpret the trend. Discuss any limitations or edge cases (e.g., sparse data, new users).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the full pipeline: data prep, TWFE DiD with user and day fixed effects and clustered SEs, interpretation of the interaction, pre-trends test with leads, discussion of late adopters and immortal time bias, Poisson count model with offset, and confidence interval. Then walk through each step methodically, emphasizing the economic meaning of the coefficient and the robustness checks. Conclude with a practical recommendation that balances statistical significance with business impact.
Pro tip: Always cluster standard errors at the level of treatment assignment (e.g., user) to account for serial correlation, and when using Poisson, include an exposure offset (e.g., log of days active) to model rates rather than counts. Also, explicitly state that the TWFE DiD estimate is a weighted average of treatment effects, which can be biased with heterogeneous effects and staggered adoption.
Load the panel data, create user and day fixed effects, and estimate the model with an interaction term between treatment and post period. Use clustered standard errors at the user level to account for within-user correlation.
Interpret the interaction coefficient as the average treatment effect on the treated (ATT) in the post period, and translate it into a percentage or absolute change in the outcome. Test for pre-trends by including leads of the treatment variable and checking if they are jointly insignificant.
Discuss how late adopters can bias TWFE estimates if effects are heterogeneous, and how immortal time bias arises when units are classified as treated before actual treatment. Suggest using methods like Sun & Abraham or Callaway & Sant'Anna to correct for these issues.
Re-estimate the model using a Poisson regression with an exposure offset (e.g., log of user-days) to model counts, and compute a 95% confidence interval for the interaction coefficient using clustered robust standard errors.
Based on the estimated effect and its confidence interval, recommend whether to roll out the treatment, iterate, or run further experiments, considering both statistical significance and business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.