This is the kind of question where you can tell they want you to go beyond 'compare buyers vs non-buyers' pretty quickly.
Start by acknowledging the self-selection bias and the need for causal inference. Propose a quasi-experimental design such as propensity score matching or instrumental variables, and discuss how you would validate assumptions and estimate the effect. Conclude with your preferred design and why it balances rigor and practicality.
Pro tip: Mention that you would combine multiple methods (e.g., PSM and DiD) to triangulate results and check for consistency, which shows robustness and maturity. Also, emphasize the importance of defining a clear engagement metric upfront and considering potential confounders like device ownership or viewing habits.
Clearly state the treatment (owning the smart speaker) and the outcome (user engagement metric, e.g., daily active minutes or content starts). Define the target estimand (ATE or ATT) and the time horizon.
List factors that influence both the decision to buy the speaker and engagement (e.g., tech-savviness, income, existing Roku usage). Discuss how these create bias in naive comparisons.
Evaluate options like propensity score matching, difference-in-differences, instrumental variables, or regression discontinuity. Select one (e.g., PSM with DiD) and justify based on data availability and assumptions.
Implement the chosen method, check covariate balance, test parallel trends (if DiD), and perform sensitivity analysis for unobserved confounders. Use bootstrapping for confidence intervals.
Translate the estimated effect into business impact, discuss limitations, and suggest next steps (e.g., a follow-up experiment if feasible).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.