Estimate Redesign Impact Using Propensity Score Matching

Q: Estimate Redesign Impact Using Propensity Score Matching

This question evaluates competency in observational causal inference and experimental design for product analytics, including familiarity with propensity score methods, matching/weighting, difference-in-differences and event-study frameworks, balance diagnostics, and sensitivity analyses within the Analytics & Experimentation domain.

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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

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?

Estimate Redesign Impact Using Propensity Score Matching

Scenario

Task

Solution

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