I went with call success rate per active user as my primary metric.
Start by defining a primary success metric that directly captures the new value of group calls, such as the percentage of active users who participate in a group call. Then select guardrails that monitor potential negative side effects on the existing 1:1 experience, overall engagement, and system health. Finally, discuss the limitations of total call volume and propose normalized alternatives like calls per user or per DAU.
Pro tip: Frame your primary metric as a 'success metric' that reflects the feature's core value, and choose guardrails that are leading indicators of long-term harm—this shows you think about both growth and sustainability. Also, explicitly state that you would validate the metric's sensitivity through an A/B test or holdout.
Choose a metric that directly measures adoption and value creation from group calls, such as the percentage of active users who initiate or join a group call within a given period.
Pick 3-5 metrics that ensure the feature doesn't harm existing user behavior or system performance, e.g., 1:1 call frequency, overall call duration, user retention, crash rate, and notification opt-outs.
Total call volume can increase simply because group calls count as one call but involve multiple users, or due to a small number of power users, masking changes in per-user behavior.
Suggest rate-based or user-normalized metrics such as calls per DAU, group calls per user, or percentage of users making group calls, which control for user growth and power-user effects.
Total call volume might be fine for capacity planning or infrastructure scaling, where aggregate load matters more than per-user behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started to sweat a little.
Start by framing the choice of randomization unit as a trade-off between bias, variance, and engineering cost, then systematically compare graph cluster randomization, ego-clusters, and geo-based switchbacks. Emphasize that the optimal choice depends on the experiment's goals, the strength of interference, and available resources, and propose a rollout plan that balances validity with practicality.
Pro tip: Acknowledge that no method is universally best; instead, recommend a hybrid or staged approach—e.g., pilot with ego-clusters for quick signal, then validate with graph clusters or switchbacks—and always quantify interference via A/A tests or holdout groups.
Clarify how interference manifests (e.g., social contagion, shared resources) and what the experiment aims to measure. Establish whether the goal is to estimate global or local effects, and the acceptable levels of bias and variance.
For each method, analyze bias (e.g., graph clusters reduce bias but may not eliminate it; ego-clusters balance bias and variance; switchbacks control temporal interference but may introduce time trends), variance (graph clusters often have high variance due to few clusters; ego-clusters lower variance; switchbacks can have low variance if well-designed), and engineering cost (graph clusters require full graph and complex randomization; ego-clusters are simpler; switchbacks need careful time segmentation and may disrupt user experience).
Choose the method that best aligns with the experiment's constraints and goals. For example, if interference is strong and the graph is known, graph cluster randomization may be preferred despite higher cost; if quick iteration is needed, ego-clusters offer a good balance; if interference is primarily temporal (e.g., supply-side), geo-based switchbacks are suitable.
Propose a phased rollout: start with a small pilot using the chosen method, monitor for interference via A/A tests or holdout groups, and gradually scale. Consider techniques like cluster-level randomization with buffer zones, time-based switchbacks with washout periods, or ego-cluster randomization with network exposure modeling.
After rollout, validate assumptions (e.g., no spillover) using diagnostics like network autocorrelation or difference-in-differences. Be prepared to adjust the randomization unit or rollout plan based on observed interference and variance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with invitation-gated treatment as my main proposal: only users who receive a group invite token are exposed, which gives you a natural instrument.
Start by acknowledging that cluster randomization is often infeasible due to network effects, interference, or operational constraints, and propose a quasi-experimental design like difference-in-differences (DiD) or synthetic control. Then, systematically walk through the key assumptions (e.g., parallel trends, no spillovers) and the diagnostics you'd run to validate them, emphasizing how you'd handle violations.
Pro tip: Show maturity by discussing how you'd combine quasi-experimental methods with sensitivity analyses (e.g., placebo tests, robustness checks) to quantify uncertainty, and mention that you'd pre-register the analysis plan to avoid p-hacking.
Select a design like difference-in-differences (DiD), synthetic control, or interrupted time series based on the treatment assignment mechanism and data availability. Justify why it's the best fit given the constraints.
Clearly articulate the assumptions required for causal inference, such as parallel trends (for DiD), no interference/spillovers, and correct model specification. Explain how each assumption relates to the design.
Describe specific diagnostics: pre-trend tests (event study plots), placebo tests (e.g., fake treatment dates), and checks for spillovers (e.g., comparing treated and control units' characteristics). Mention statistical tests and visualizations.
If assumptions are violated, discuss remedies like propensity score matching, synthetic control, or instrumental variables. Conduct sensitivity analyses (e.g., Rosenbaum bounds, different control groups) to assess robustness.
Quantify the treatment effect with confidence intervals, and clearly communicate limitations and the strength of causal evidence. Suggest follow-up experiments if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Creators vs joiners distinction is important here and I made sure to flag it early.
Start by defining the target population and the precise moment users become eligible for the experiment, then explain how you assign them to treatment or control. Next, outline your exclusion criteria to remove invalid or biased users, and finally describe the technical and analytical safeguards you use to prevent contamination between groups.
Pro tip: Emphasize that you document all decisions before launch and monitor for contamination post-launch using metrics like cross-group exposure rates; this shows you understand that experimentation is an ongoing process, not a one-time setup.
Specify the exact user action or system event that triggers inclusion in the experiment, and define what constitutes 'exposure' to the treatment (e.g., seeing the new feature). Ensure the trigger is aligned with the experiment's goal and is measurable.
Identify users or events to exclude, such as bots, internal employees, users who previously participated in similar experiments, or those with incomplete data. Justify each exclusion based on potential bias or interference.
Implement randomization at the appropriate unit (e.g., user, session) and use techniques like unique user IDs, cookie-based bucketing, or network isolation to prevent users from being exposed to both treatment and control. Consider spillover effects and use cluster randomization if needed.
After launch, check for contamination by analyzing exposure logs, ensuring users are consistently in one group, and monitoring for unexpected cross-group interactions. Use holdout groups or A/A tests to validate the setup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pre-registration piece was fine: lock in primary metric, guardrails, sample size calculation, and analysis plan before launch.
Start by framing the pre-registration as a way to ensure rigor and avoid p-hacking, then walk through the key components: defining the metric and hypothesis, calculating the minimum detectable effect (MDE) based on plausible baselines, addressing seasonality through design and analysis, and mitigating novelty and ramp-up effects via run-in periods and extended measurement. Emphasize that pre-registration includes specifying these details in advance to maintain statistical validity.
Pro tip: Show that you understand the business context: at Meta, even small effect sizes can matter at scale, so balance statistical power with practical significance when choosing MDE. Also, mention that pre-registration should include a plan for multiple comparisons and sequential testing if applicable.
Clearly state the null and alternative hypotheses, and select a primary metric that is sensitive to the change and aligned with business goals. Specify guardrail metrics to monitor for unintended consequences.
Estimate the baseline metric value from historical data or a pilot. Choose an MDE that is the smallest effect size that would justify the cost of implementation, considering statistical power (typically 80%) and significance level (typically 5%).
Use historical data to identify seasonal patterns and either stratify randomization by time or include time as a covariate in the analysis. Alternatively, run the experiment long enough to cover full seasonal cycles.
Implement a ramp-up period where the treatment is gradually rolled out, and exclude the initial days from analysis to let user behavior stabilize. Use a holdback group to measure long-term effects and detect novelty decay.
Document all decisions—metric definitions, MDE, sample size, duration, exclusion criteria, and statistical tests—in a pre-registration document before data collection. This prevents post-hoc rationalization and ensures transparency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.