Evaluate business value of lower ETA

Uber wants to evaluate a marketplace intervention that reduces ETA, defined as the estimated number of minutes from a rider's request until the driver arrives at the pickup location. Assume this change could affect both rider demand and driver marketplace dynamics. Answer the following: 1. Which metrics would you track? Define primary metrics, secondary metrics, and guardrail metrics. 2. From a business perspective, why is reducing ETA important? 3. Suppose you observe a **positive correlation** between ETA and **session conversion rate**, where session conversion rate is the share of rider app sessions that end in a successful ride request. How would you interpret this finding? What confounders or biases could explain it? 4. How would you design an experiment to estimate the **causal impact** of reducing ETA? Discuss when a **switchback experiment** is appropriate and when **synthetic control** would be useful. 5. After the experiment, the 95% confidence interval for the lift in session conversion rate is `[-5%, +1%]`. How would you interpret this result? What would you do to improve precision in a future test? 6. Assume the team instead ran a **user-level A/B test**. During the readout, the PM asks for a deep dive on users who completed **at least 5 trips during the experiment**. How would you respond, and how would you communicate any methodological concerns?