Start by clarifying the goal: to measure the impact of identity verification with trust signals on user trust and safety outcomes, while considering network effects and rare events. Then outline a randomized experiment design that accounts for potential biases and defines success metrics, including guardrails. Finally, discuss how to interpret results, especially when metrics move in different directions.
Pro tip: In two-sided markets like Coinbase, always consider network effects and interference between users; consider cluster randomization or switchback tests when individual randomization is not feasible. Also, for rare events, use surrogate metrics or longer horizons to ensure sufficient power.
Clarify the primary goal: increase trust and safety without harming user experience. Formulate hypotheses about how verified badges and warnings affect user behavior, such as increased trust leading to higher transaction completion or reduced fraud.
Choose the randomization unit (user, session, or cluster) based on interference risk. For network effects, consider cluster randomization by social graph or geographic region. Ensure control and treatment groups are comparable and define exposure.
Define primary metrics (e.g., trust score, transaction success rate) and guardrail metrics (e.g., false positives, user complaints). For rare events like fraud, use surrogate metrics (e.g., suspicious activity reports) or extend the experiment duration to accumulate enough events.
Consider biases such as selection bias (if randomization is flawed), novelty effects, and network effects. Use techniques like stratified randomization, pre-period matching, or switchback tests to mitigate.
If metrics conflict (e.g., trust increases but transaction volume drops), segment by user type or behavior. Use causal inference methods to understand trade-offs and decide whether to iterate, launch, or abandon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.