##### Scenario A mobile app is redesigned, but the new version is only adopted by users who choose to upgrade; the team needs to measure performance impact. ##### Question Without a forced A/B test, how would you estimate the causal impact of the redesign? How would you define comparable treatment and control groups of ‘new-version’ and ‘old-version’ users? Which user features beyond engagement/behavior would you include to ensure similarity? What statistical or causal-inference methods would you apply, and how would you validate assumptions? ##### Hints Discuss propensity scores, matching, weighting, diff-in-diff, covariate balance checks, sensitivity analyses.

Estimate Redesign Impact Using Propensity Score Matching evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at LinkedIn.

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This question is commonly asked for Data Scientist candidates at LinkedIn during technical interviews.

Estimate Redesign Impact Using Propensity Score Matching

Scenario

A mobile app has been redesigned. Adoption is voluntary: users choose to upgrade to the new version over time. The team needs to estimate the redesign's causal impact on key outcomes (e.g., engagement, retention, revenue) without a forced A/B test.

Task

Design an observational causal-inference approach to estimate the impact of the redesign. Address the following:

Defining Groups

How will you define comparable treatment (new-version) and control (old-version) users and the time windows for analysis?

Covariates

Which user features beyond engagement/behavior will you include to improve similarity between groups?

Methods

Which statistical/causal methods will you apply (e.g., propensity scores, matching/weighting, difference-in-differences, event studies), and why?

Assumption Checks and Validation

How will you check covariate balance, validate identifying assumptions (e.g., parallel trends), and run sensitivity analyses for unobserved confounding?

Robustness

How will you handle issues like staggered adoption, attrition/churn, potential interference/spillovers, metric instrumentation changes, and heterogeneous effects?

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?