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Meta·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026

Summary

A deep causal inference case for Meta DS, focused on designing a study around parental account linking for teens. The question was massive and covered basically every angle of experimental design you can think of. Felt more like a take-home than a live interview.

Questions Asked (1)

Q1

Meta wants to let parents register and link to their teen's account. Design a study to estimate the causal effect of parental registration on teen well-being and engagement. Cover metrics, experimental design, power, measurement, validity threats, a quasi-experimental fallback, and ethics.

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Sense & Ideation
Author's notes

This was a monster.

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Suggested Approach

Start by defining clear metrics for teen well-being and engagement, then propose a randomized controlled trial where parents are encouraged to register and link to their teen's account. Address power analysis, measurement validity, and potential threats, and outline a quasi-experimental fallback and ethical considerations.

Pro tip: Emphasize that parental registration is a two-sided intervention affecting both parents and teens, so you must consider spillover effects and design the experiment at the family level. Also, proactively discuss how you would measure well-being without disrupting the user experience, perhaps using validated survey instruments and behavioral proxies.

1. Define Metrics and Hypotheses

Identify primary and secondary metrics for teen well-being (e.g., self-reported happiness, stress levels) and engagement (e.g., daily active minutes, sessions). Clearly state the causal hypothesis: parental registration improves teen well-being and engagement.

2. Design the Experiment

Propose a randomized controlled trial at the family level, randomly assigning families to treatment (encouraged to register) or control. Specify randomization unit, treatment implementation, and duration.

3. Power Analysis and Sample Size

Conduct a power analysis to determine the required sample size, considering expected effect sizes, variance, and intra-family correlation. Discuss how to handle multiple comparisons.

4. Measurement and Validity

Detail how you will measure well-being (e.g., in-app surveys, passive data) and engagement (log data). Address validity threats such as selection bias, attrition, and novelty effects, and how to mitigate them.

5. Quasi-Experimental Fallback and Ethics

If randomization is infeasible, propose a quasi-experimental design (e.g., difference-in-differences with staggered rollout). Discuss ethical considerations: informed consent, privacy, and potential risks to teens.

Key Points to Mention

  • Randomization at the family level to avoid contamination between parent and teen.
  • Use of validated well-being scales (e.g., WHO-5) and behavioral engagement metrics.
  • Power analysis accounting for clustering and minimum detectable effect.
  • Threats to validity: non-compliance, attrition, and spillover effects.
  • Quasi-experimental methods like difference-in-differences or instrumental variables.
  • Ethical safeguards: parental consent, teen assent, data privacy, and monitoring for harm.

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