iOS vs. Android Usage Gap: Modeling, Causality, Telemetry, Missing Data, and Segmented Actions

Context

You observe that Instagram usage is substantially higher among iOS users than Android users. Assume "usage" refers to per-user weekly minutes spent in app (or a similar engagement proxy). Your task is to: model the drivers of usage, assess whether OS has a causal role, design telemetry comparisons across OS versions, handle missing data appropriately, and propose actions for discovered segments.

Tasks

1. Supervised learning framing
   • Define a clear prediction target for user-level usage.
   • List candidate features across: demographics, engagement, acquisition channel, and device/app telemetry.
   • Choose and justify a modeling approach (decision tree vs. random forest) for identifying discriminant variables.
2. Causality check: OS as cause vs. proxy
   • Explain how you would test whether OS is causal vs. a proxy for other factors.
   • Outline procedures for feature ablation, conditional permutation importance, and propensity stratification.
3. Telemetry collection and cross-OS comparison
   • Specify additional telemetry to collect (e.g., cold-start latency, crash rate, time-to-first-paint, battery drain).
   • Describe how to compare distributions across OS versions robustly.
4. Missing data handling
   • Defend the "-1 sentinel" approach vs. model-based imputation.
   • Explain when each is appropriate and any guardrails.
5. Segment actions and validation
   • Given two segments: (a) Argentinians underperforming, (b) Indians <30 overperforming, propose concrete product/marketing next steps.
   • Explain how to validate impact causally.