Amazon is considering adding live broadcasts of selected sports events to Prime Video. Using observational data, estimate the causal impact on Prime membership subscriptions and engagement. Precisely specify: (1) treatment and control at the user level (e.g., users exposed to/actually watching live sports vs not), (2) primary outcomes (e.g., subscription starts, retention, upgrades, watch‑time), and (3) key covariates for selection on observables. Start with matching: choose a method (k‑NN, caliper, Mahalanobis, or propensity‑score matching), define distance or the propensity model class, explain overfitting controls (regularization, cross‑fitting), and provide balance diagnostics you will require (SMD thresholds, variance ratios, overlap plots). State assumptions (unconfoundedness, overlap, SUTVA) and how you will test/justify them. Then propose an instrument to address unobservables via randomized variation in promotion prominence for the live stream (e.g., hero banner vs standard tile). Write the 2SLS explicitly: First stage Z→LiveWatch with controls and fixed effects; second stage LiveWatch_hat→Outcome with the same controls. Discuss IV validity (relevance, exclusion, independence, monotonicity), weak‑IV checks (first‑stage F), and over‑identification tests if multiple instruments. Finally, outline a DID/event‑study alternative using staggered rollout, detail the regression, fixed effects, and heterogeneity, and list key threats (interference, spillovers, time‑varying confounding) with mitigation.