Measure Causal Impact of Self-Selected App Redesign

Q: Measure Causal Impact of Self-Selected App Redesign

This question evaluates a data scientist's competency in causal inference and observational study design, specifically the ability to define estimands and identification assumptions for self-selected, staggered adoption of a product change.

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Question

Measuring Causal Impact of an Opt-in Mobile App Redesign

Context

A mobile app ships a redesigned UI as a new version. Users can choose to upgrade (opt-in). Because adoption is self-selected and staggered, a classic randomized A/B test is not feasible.

Question

How would you measure the causal impact of the redesign when users self-select into the new version?

Describe:

The causal-inference framework you would use (estimand, identification assumptions).
How you would construct comparable treatment and control groups.
Which features you would match or weight on besides past engagement.
How you would validate assumptions and perform robustness checks (e.g., propensity-score methods, balance checks, difference-in-differences, event study).

Measure Causal Impact of Self-Selected App Redesign

Measuring Causal Impact of an Opt-in Mobile App Redesign

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Measure Causal Impact of Self-Selected App Redesign

Overview

Measuring Causal Impact of an Opt-in Mobile App Redesign

Context

Question

Solution

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