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Estimate Redesign Impact Using Propensity Score Matching

Last updated: Mar 29, 2026

Quick 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.

  • medium
  • LinkedIn
  • Analytics & Experimentation
  • Data Scientist

Estimate Redesign Impact Using Propensity Score Matching

Company: LinkedIn

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### 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.

Quick Answer: 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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LinkedIn logo
LinkedIn
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
118
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:

  1. Defining Groups
  • How will you define comparable treatment (new-version) and control (old-version) users and the time windows for analysis?
  1. Covariates
  • Which user features beyond engagement/behavior will you include to improve similarity between groups?
  1. Methods
  • Which statistical/causal methods will you apply (e.g., propensity scores, matching/weighting, difference-in-differences, event studies), and why?
  1. Assumption Checks and Validation
  • How will you check covariate balance, validate identifying assumptions (e.g., parallel trends), and run sensitivity analyses for unobserved confounding?
  1. 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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