Start by clarifying the data structure and assumptions, then outline a step-by-step algorithm: identify treatment and control groups, compute pre-period differences, select the latest pre-period and earliest post-period for the DiD estimate, and finally validate parallel pre-trends by checking the maximum absolute difference in treatment-control gaps across all pre-periods against the threshold. Emphasize the importance of data validation and edge cases.
Pro tip: Mention that you would first verify the data is balanced and that the treatment group is clearly defined; also, consider using a regression framework with interaction terms to obtain standard errors, which is often expected in industry settings.
Confirm the structure: each period has treatment and control groups, and outcomes are measured. Ensure the treatment group is consistently defined and that periods are ordered.
For each pre-period, calculate the difference in mean outcomes between treatment and control groups. Store these differences for later use.
Identify the latest pre-period and earliest post-period. Compute the DiD estimate as (post_treatment - post_control) - (pre_treatment - pre_control) using the selected periods.
Compute the maximum absolute difference between any two pre-period treatment-control differences. If this max gap is within the given threshold, conclude parallel pre-trends hold; otherwise, flag a violation.
Address missing data, unequal group sizes, or insufficient periods. Report the DiD estimate, the max pre-trend gap, and whether the threshold was met.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.