Estimate sales impact from reviews causally

Your PM asks: Do better product reviews cause higher sales, or do higher sales lead to more reviews? Design an analysis to estimate the causal effect of reviews on sales. Deliverables: 1) define outcome(s) and treatment(s) (e.g., average rating, review count, recent reviews, helpful votes) and the time windows; 2) enumerate key confounders (seasonality, promotions, price changes, stockouts, product life cycle) and how you’ll control for them; 3) propose an experiment (e.g., vary review visibility/order or badging) with primary metrics, guardrails, and sample size calculations; 4) if experimentation is infeasible, propose a quasi-experimental design (difference-in-differences using staggered review-widget rollout, or regression discontinuity at visibility thresholds like the first visible star) and a complementary observational model (panel regression with product and time fixed effects, lagged ratings, and instrument ideas); 5) specify checks for reverse causality, selection bias, and survivorship; 6) show how you would report the effect size (elasticity: % sales change per +0.1 rating) with confidence intervals and how to validate robustness.