Estimate Redesign Impact Using Propensity Score Matching
Overview
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.
LinkedIn
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
83
0
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?
Solution
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