This is the kind of question where you nod and think you know the answer and then halfway through your response you realize you're just describing the problem, not solving it.
Start by defining what interference means in the context of A/B testing—when one user's treatment affects another's outcome—and explain why it's especially relevant for a social app like Tinder. Then walk through a structured process: identify potential interference, measure its impact, and apply mitigation techniques such as cluster randomization or switchback tests. Emphasize that the goal is to preserve the validity of causal inference while balancing practical constraints.
Pro tip: Mention that interference is not just a nuisance but can be a signal of network effects; in some cases, you can design experiments to measure and even leverage those effects (e.g., using ego-network randomization). This shows you understand the product's social dynamics and can turn a challenge into an opportunity.
Clarify what interference means in your experiment context (e.g., spillover, contagion, or network effects) and identify where it might occur in the user journey or system architecture.
Quantify the potential bias by analyzing metrics across treatment and control groups, checking for anomalies like unexpected lifts in control or correlations between user interactions.
Select an appropriate design such as cluster randomization (randomizing by social graph clusters), switchback tests (time-based randomization), or using a holdout group to isolate effects.
Run power analyses and sanity checks to ensure the chosen method reduces interference, and be prepared to adjust the design if interference persists or new sources emerge.
Explain the trade-offs between reducing interference and other factors like statistical power, cost, or speed, and align with stakeholders on the acceptable level of risk.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.