##### Scenario DoorDash's account-partner team acquires new merchants onto the marketplace, and leadership wants to quantify how well that program works. Because there is no randomized experiment, you must rely on observational data, and the partners tend to concentrate on a single cuisine (e.g., seafood restaurants). ##### Question 1. Which core success metrics would you report to evaluate account-partner onboarding performance? 2. Without randomized experiments, how would you estimate the causal impact of the program (e.g., using difference-in-differences)? 3. If account partners mainly sign seafood restaurants, how would you build a propensity-score or synthetic-control comparison group, and which merchant attributes would you match on? 4. How would you model the relationship between acquisition cost (or spend) and incremental revenue, and why might a logistic (S-shaped) curve be more appropriate than a linear one? ##### Hints Think causal inference: parallel-trend checks, matching, and merchant-level covariates (location, cuisine, size, tenure). For the spend-response curve, reason about diminishing returns and bounded (TAM-constrained) outcomes.