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Use DiD for staggered treatment adoption | TikTok Interview Question

Quick Overview

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.

Staggered DiD for a Weekly RPU Rollout (50 Regions, 2025-06-01 to 2025-08-15)

Context and assumptions:

You have panel data at the region-week level with weekly revenue per user (RPU) as the outcome.
A product/feature is rolled out once per region during 2025-06-01 to 2025-08-15 and remains active thereafter (no reversals). Assume no never-treated regions; at any week t, the valid controls are regions not yet treated (G_i > t). You also have a pre-period (e.g., Jan–May 2025) to test pre-trends.
Goal: estimate the causal effect of the rollout on RPU in the presence of potentially heterogeneous and dynamic effects across cohorts and over time since adoption.

Tasks: (a) State when two-way fixed effects (TWFE) is biased under staggered adoption and what it identifies. (b) Implement a heterogeneous-effects-robust estimator (Callaway–Sant’Anna or Sun–Abraham), define group-time ATT ATT(g, t), and specify aggregation weights to obtain overall and dynamic effects. (c) Specify an event-study setup with a clear reference period and how you would visualize it. (d) Describe pre-trend testing (joint F-test on leads) and remedies if violated. (e) Explain inference with few clusters, including wild cluster bootstrap and handling serial correlation. (f) Explain how you would reconcile DiD estimates with a parallel propensity score matching (PSM) analysis.

Quick Overview

Staggered DiD for a Weekly RPU Rollout (50 Regions, 2025-06-01 to 2025-08-15)

Context and assumptions:

You have panel data at the region-week level with weekly revenue per user (RPU) as the outcome.

A product/feature is rolled out once per region during 2025-06-01 to 2025-08-15 and remains active thereafter (no reversals). Assume no never-treated regions; at any week t, the valid controls are regions not yet treated (G_i > t). You also have a pre-period (e.g., Jan–May 2025) to test pre-trends.

Goal: estimate the causal effect of the rollout on RPU in the presence of potentially heterogeneous and dynamic effects across cohorts and over time since adoption.

Use DiD for staggered treatment adoption

Quick Overview

Staggered DiD for a Weekly RPU Rollout (50 Regions, 2025-06-01 to 2025-08-15)

Solution

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Use DiD for staggered treatment adoption

Quick Overview

Staggered DiD for a Weekly RPU Rollout (50 Regions, 2025-06-01 to 2025-08-15)

Solution

Comments (0)