Meta·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta DS interview with a single very involved causal inference question about estimating the effect of a free employee shuttle program across hundreds of sites. The whole thing was basically a 45-minute deep dive into one problem, which I was not fully prepared for in terms of breadth.

Questions Asked (1)

You have site-level data from 1,000+ locations, several hundred of which adopted a free employee shuttle at different points in time. Design a full causal analysis to estimate the shuttle's effect on employee participation and engagement, covering your estimand, identification strategy, parallel trends checks, confounders, clustering, how you handle never-adopters and late adopters, robustness checks, and how you'd communicate results to non-technical stakeholders.

A/B Testing & ExperimentationProduct Analytics & MetricsStakeholder Management

Author's notes

This question basically ate the entire interview.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Frame the problem as a staggered difference-in-differences (DiD) design, clearly defining the estimand as the average treatment effect on the treated (ATT) for adopters. Walk through identification assumptions, diagnostics, and robustness checks, then translate findings into actionable insights for non-technical stakeholders.

Pro tip: Emphasize that with staggered adoption, traditional two-way fixed effects (TWFE) can be biased; propose modern estimators like Callaway & Sant'Anna or Sun & Abraham, and discuss how to handle never-adopters as a clean comparison group.

1. Define the estimand and causal question

Specify the target parameter: the average treatment effect of the shuttle on participation and engagement for locations that adopted it (ATT). Clarify whether you're interested in the effect at a specific time or aggregated over post-adoption periods.

2. Choose identification strategy and model

Use a staggered DiD design with never-adopters as controls. Consider modern estimators (e.g., Callaway & Sant'Anna, Sun & Abraham) to avoid TWFE bias. Include location and time fixed effects, and cluster standard errors at the location level.

3. Check parallel trends and address confounders

Test pre-trends using event-study plots and placebo tests. Control for time-varying confounders (e.g., local labor market conditions, site size, industry) and consider propensity score matching or synthetic control if adopters differ systematically.

4. Handle never-adopters and late adopters, and run robustness checks

Use never-adopters as a clean control group; for late adopters, either exclude them or use them as not-yet-treated controls in a staggered design. Conduct robustness checks: alternative estimators, different clustering levels, sensitivity to unobserved confounders, and placebo outcomes.

5. Communicate results to non-technical stakeholders

Translate findings into business impact: e.g., 'Shuttle adoption increased participation by X% and engagement by Y%, equivalent to Z additional engaged employees per site.' Use visualizations (event-study plots, effect sizes with confidence intervals) and avoid jargon.

Key Points to Mention

Staggered difference-in-differences and the pitfalls of TWFE with heterogeneous treatment timing
Parallel trends assumption and event-study diagnostics
Clustering standard errors at the location level to account for within-site correlation
Handling never-adopters as controls and late adopters via not-yet-treated groups
Robustness checks: alternative estimators, placebo tests, sensitivity analysis
Translating causal estimates into actionable business metrics for stakeholders

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.