LinkedIn·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

LinkedIn data scientist interview with a meaty causal inference question about a mobile app redesign rollout. No forced experiment, so you have to reason your way through observational methods. Solid technical depth required.

Questions Asked (1)

A mobile app is redesigned but users self-select into the new version. Without running a forced experiment, how would you estimate the causal impact of the redesign? How do you define treatment and control groups, which user features would you use to ensure comparability, and what methods would you apply to validate your assumptions?

A/B Testing & ExperimentationProduct Analytics & Metrics

Author's notes

This one is where I started okay and then slowly dug myself a hole.

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AI HintsAI Generated

Suggested Approach

Acknowledge the self-selection bias and propose a quasi-experimental design such as propensity score matching or instrumental variables to estimate the causal effect. Define treatment as users who opted into the redesign and control as those who did not, then use pre-treatment covariates to balance the groups and validate assumptions through sensitivity analyses.

Pro tip: Emphasize that self-selection often correlates with unobserved factors like tech-savviness or engagement; consider using an instrumental variable like a random prompt to opt-in, or a difference-in-differences design if you have pre-period data. Always quantify the bias and discuss limitations transparently.

1. Define treatment and control groups

Treatment group: users who self-selected into the new version. Control group: users who remained on the old version. Ensure both groups are defined based on the same time window and user eligibility criteria.

2. Identify confounders and select covariates

Choose pre-treatment user features that predict both self-selection and the outcome, such as demographics, historical engagement metrics, device type, and prior app usage patterns. These will be used to balance groups.

3. Apply causal inference methods

Use propensity score matching, weighting, or stratification to create comparable groups. Alternatively, consider instrumental variables (e.g., random opt-in prompts) or regression discontinuity if applicable. For panel data, difference-in-differences can control for time-invariant confounders.

4. Validate assumptions and assess robustness

Check covariate balance after matching (e.g., standardized mean differences). Conduct sensitivity analysis for unobserved confounding (e.g., Rosenbaum bounds). If using IV, test instrument relevance and exogeneity. Compare results across methods.

5. Estimate and interpret the causal effect

Compute the treatment effect on key metrics (e.g., engagement, retention) using the chosen method. Report confidence intervals and discuss practical significance. Acknowledge limitations and potential biases.

Key Points to Mention

Self-selection bias and why it invalidates naive comparisons
Propensity score matching or weighting to balance observed covariates
Instrumental variables (e.g., random encouragement to opt-in) to address unobserved confounding
Difference-in-differences if pre-period data is available
Sensitivity analysis for unobserved confounders (e.g., Rosenbaum bounds)
Covariate balance checks and common support

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