Use DiD for staggered treatment adoption
TikTokData Scientist · Technical Screen
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This question evaluates proficiency in causal inference for panel data, specifically difference-in-differences with staggered adoption, heterogeneous and dynamic treatment effects, event-study design, pre-trend diagnostics, cluster-robust inference, and comparison with propensity-score matching.
You are running a staggered rollout across 50 regions from 2025-06-01 to 2025-08-15 with weekly revenue per user as the outcome. Design a DiD analysis robust to heterogeneous treatment effects. Specify: (a) when two-way fixed effects (TWFE) is biased and what it identifies; (b) implement Callaway–Sant’Anna or Sun–Abraham, including group-time ATT and aggregation weights; (c) event-study setup with a proper reference period and how you’d visualize it; (d) pre-trend testing (joint F-test) and remedies if violated; (e) inference with few clusters (wild cluster bootstrap) and serial correlation; (f) how you would reconcile DiD estimates with a parallel PSM analysis.
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