Start by framing the causal question as the effect of adding live sports to Prime Video on Prime membership subscriptions and engagement. Then systematically walk through each requested component: define treatment and control groups, specify outcomes and covariates, outline a matching strategy with balance checks, discuss assumptions and defenses, present an IV approach using promotion placement with full 2SLS setup, and finally describe a diff-in-diff or event study design leveraging staggered rollout. Emphasize the importance of addressing confounding and selection bias throughout.
Pro tip: Acknowledge that observational data may suffer from selection bias and that the IV and diff-in-diff approaches provide complementary evidence; triangulating results across methods strengthens causal claims. Also, mention that you would pre-register the analysis plan to avoid p-hacking and ensure robustness.
Clearly define the treatment as the introduction of live sports content on Prime Video, and control as regions or time periods without it. Specify primary outcomes: change in Prime membership subscriptions (e.g., new sign-ups, retention) and engagement metrics (e.g., watch time, frequency).
Select covariates that affect both treatment assignment and outcomes, such as regional demographics, prior streaming behavior, and marketing spend. Use propensity score matching or coarsened exact matching to create comparable groups, and assess balance via standardized mean differences and variance ratios.
State key assumptions: conditional independence (no unmeasured confounding), stable unit treatment value assumption (SUTVA), and positivity. Defend them by arguing that rich covariates capture relevant confounders, and conduct sensitivity analyses (e.g., Rosenbaum bounds) to assess robustness to hidden bias.
Use variation in promotion placement (e.g., random featuring of sports content on the Prime Video homepage) as an instrument for treatment. Set up 2SLS: first stage regresses treatment on the instrument and covariates; second stage regresses outcomes on predicted treatment. Discuss exclusion restriction and test for weak instruments (e.g., F-statistic > 10).
Leverage staggered rollout across regions or time to implement a difference-in-differences design. Include unit and time fixed effects, and test for parallel pre-trends. Alternatively, conduct an event study to estimate dynamic treatment effects and check for anticipation effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.