Roblox·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Roblox DS interview with a meaty causal inference question dressed up as a product metrics problem. No fluff, they went straight into the weeds on observational analysis.

Questions Asked (1)

Roblox wants to know if players genuinely prefer games made by local creators. Define the primary and secondary metrics you'd use to measure this preference, and since a clean A/B test isn't feasible here, walk through an analysis plan that can credibly estimate the causal effect without just picking up selection bias.

A/B Testing & ExperimentationProduct Analytics & MetricsData Modeling

Author's notes

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Suggested Approach

Start by defining 'local creators' and 'preference' operationally, then propose primary and secondary metrics that capture engagement and satisfaction. For causal estimation, outline a quasi-experimental design such as a natural experiment or instrumental variable, and detail robustness checks to address selection bias.

Pro tip: Emphasize that you would validate the causal estimate by testing for pre-trends and using negative controls; this shows you understand the limitations of observational data and the importance of falsification tests.

1. Define constructs and metrics

Clearly define what 'local creator' means (e.g., based on geography or community) and what 'preference' entails (e.g., engagement, retention, satisfaction). Propose a primary metric like average session time or retention rate for local vs. non-local games, and secondary metrics like like/dislike ratio, repeat play rate, or survey-based preference scores.

2. Identify causal challenge and potential designs

Acknowledge that a clean A/B test is infeasible because you can't randomly assign players to prefer local games. Discuss potential quasi-experimental designs: difference-in-differences (if a policy change affected local game availability), instrumental variables (e.g., using creator location as an instrument for local game exposure), or regression discontinuity (if there's a threshold for 'local' status).

3. Outline analysis plan and assumptions

Detail the chosen design: specify the model, key assumptions (e.g., parallel trends for DiD, exclusion restriction for IV), and how you would test them. Include steps to control for confounders like game quality, player demographics, and time trends.

4. Address selection bias and robustness

Explain how the design mitigates selection bias (e.g., IV isolates exogenous variation). Propose robustness checks: placebo tests, sensitivity analysis, and alternative specifications. Also suggest triangulating with qualitative data or surveys.

5. Interpret and communicate results

Discuss how to interpret the causal estimate in terms of effect size and practical significance. Emphasize the need to communicate uncertainty and limitations to stakeholders, and suggest next steps like a follow-up experiment if possible.

Key Points to Mention

Primary metric: engagement (e.g., session length, retention) or preference (e.g., like ratio); secondary metrics: repeat play rate, survey responses, social sharing.
Selection bias: players who choose local games may differ systematically; need to isolate causal effect.
Quasi-experimental methods: difference-in-differences, instrumental variables, regression discontinuity, propensity score matching.
Assumption testing: parallel trends, exclusion restriction, balance checks.
Robustness: placebo tests, negative controls, sensitivity analysis.
Triangulation: combine quantitative causal estimates with qualitative user feedback or surveys.

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