Start by clarifying the schema and assumptions, then outline a step-by-step SQL plan that builds the user-week panel. Emphasize the importance of correctly defining treatment timing, applying the exclusion criteria, and aggregating metrics at the user-week level while maintaining the panel structure for difference-in-differences analysis.
Pro tip: Mention that you would validate the panel by checking for balanced pre/post periods and ensuring no spillover effects from excluded households. Also, consider using window functions to efficiently compute first reminder dates and household-level exclusions.
Identify the tables and columns needed: users, reminders, CSAT scores, session data, and purchases. Define the treatment date as each user's first reminder date using a MIN aggregation or window function.
Exclude users whose household has an earlier-treated member. This requires joining users to households, finding the minimum first reminder date per household, and filtering out users whose own first reminder date is not the household minimum.
For each treated user, generate a series of weeks from 4 weeks before to 3 weeks after the treatment date (or 8 weeks total). Use a date dimension or generate_series to create the weekly panel.
Join the weekly panel with CSAT, session, and purchase data, aggregating metrics per user per week. Ensure that weeks with no data are filled with zeros or nulls as appropriate.
Include columns for user_id, week relative to treatment, treatment indicator (1 if post-treatment, 0 otherwise), and the aggregated metrics. This structure supports difference-in-differences analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the panel structure and treatment/control assignment, then write a SQL query that aggregates user-week data into four cells (treated/control × pre/post) and computes the difference-in-differences. Emphasize that the DiD estimate is the interaction between time and treatment, and discuss assumptions like parallel trends.
Pro tip: Mention that you would check for pre-treatment parallel trends and consider clustering standard errors at the user level to account for repeated observations, which is crucial for valid inference in panel data.
Confirm the user-week panel schema, treatment assignment, and pre/post period definitions. State the parallel trends assumption and that treatment is randomly assigned.
Write a subquery or CTE that computes average outcome for each group (treated/control) and period (pre/post), yielding four mean values.
Calculate the difference in means for treated (post - pre), the difference for control (post - pre), and then subtract the control difference from the treated difference to get the DiD estimate.
Check that the query returns the expected four cells and that the DiD estimate aligns with manual calculation. Discuss statistical significance and potential confounders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I defaulted to sklearn for the logit and wrote a manual nearest-neighbor loop, which felt clunky.
Start by framing the problem: propensity scores estimate the probability of treatment given covariates, used to balance groups in observational studies. Then walk through the code steps: fit logistic regression, compute scores, perform 1:1 nearest-neighbor matching with a caliper, and assess balance via standardized mean differences (SMD) before and after. Emphasize validation of overlap and balance, and mention trade-offs like caliper choice and matching without replacement.
Pro tip: Always check the distribution of propensity scores and overlap before matching; if there's poor overlap, consider trimming or using alternative methods like IPTW. Also, report SMD for all covariates, not just the ones in the model, to ensure balance.
Use logistic regression with treatment as outcome and baseline covariates (device, country, pre-period usage) as predictors. Encode categorical variables appropriately (e.g., one-hot encoding) and check for multicollinearity.
Predict probabilities for all units. Plot histograms of scores for treated and control groups to check common support. Consider trimming if overlap is poor.
For each treated unit, find the closest control unit in propensity score within a caliper of 0.05. Use matching without replacement to avoid reuse. Implement via libraries like `sklearn` or custom code.
Calculate SMD for each covariate before and after matching. SMD = (mean_treated - mean_control) / pooled_std. Aim for SMD < 0.1 after matching to indicate good balance.
Present SMD before and after matching in a table or plot. Discuss any remaining imbalance and potential sensitivity analyses (e.g., different calipers, matching with replacement).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.