This is where I started to feel the ground shift.
Start by clarifying the causal question: define the estimand as the average effect of assigning the Group Call feature to clusters (e.g., groups or social neighborhoods) to capture intent-to-treat (ITT) under interference. Then choose the exposure unit as the cluster (group) to align with the randomization unit and avoid contamination, and discuss how to estimate per-user effects via cluster-level ITT divided by cluster-level engagement (complier average causal effect) if needed.
Pro tip: Emphasize that under interference, the estimand must be defined at the randomization unit level, and per-user effects are only interpretable as a weighted average of cluster-level effects; avoid claiming a pure per-user ATE without strong assumptions.
Identify how interference occurs (e.g., calls within groups affect non-treated members) and what decision the experiment informs (launch, targeting). This determines whether the estimand should be at cluster or user level.
Choose the exposure unit as the cluster (e.g., group, social neighborhood) where interference is contained, and randomize at that same level to ensure valid inference. Explain why user-level randomization would violate SUTVA.
Define the primary estimand as the average treatment effect of assignment at the cluster level (ITT). If a per-user effect is desired, define it as a complier average causal effect (CACE) or a weighted average of cluster-level effects, acknowledging assumptions.
Use cluster-level analysis (e.g., t-test on cluster means) or mixed-effects models with cluster random effects. For per-user CACE, use instrumental variables or two-stage least squares with cluster-level assignment as instrument.
Check for spillovers across clusters, partial interference, and cluster size variability. Report both ITT and CACE with confidence intervals, and discuss generalizability to the target population.
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Start by defining the social graph and the criteria for strong ties, then explain how you would weight edges based on interaction frequency, recency, and reciprocity. Next, describe a thresholding approach to filter weak edges, apply connected components to form clusters, and outline methods to handle overlaps such as assigning nodes to the cluster with the strongest connection or using a resolution parameter in community detection algorithms.
Pro tip: Emphasize that cluster construction should align with the experiment's goals—e.g., minimizing interference in A/B tests—and mention that you'd validate clusters by checking metrics like modularity or conductance, and consider using overlapping community detection if the experiment allows.
Identify signals of strong ties (e.g., frequent interactions, mutual friends, recent communication) and assign weights to edges accordingly, possibly using a composite score.
Select a method like thresholding edge weights followed by connected components, or use community detection algorithms (e.g., Louvain, Leiden) that naturally handle weighted graphs.
Decide on a strategy: either enforce disjoint clusters by assigning each node to one cluster (e.g., via label propagation or greedy assignment) or allow overlap if the experiment permits, using methods like clique percolation.
Evaluate cluster quality using metrics like modularity, conductance, or size distribution, and adjust thresholds or algorithms to meet experiment requirements (e.g., balanced cluster sizes).
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Start by acknowledging the core problem: cluster randomization creates spillover and contamination at cluster boundaries. Then explain how holdout buffers and partial saturation designs mitigate these issues by creating geographic or temporal separation between treatment and control units, and discuss the trade-offs in terms of statistical power and bias.
Pro tip: Emphasize that the choice depends on the expected spillover mechanism and the cost of contamination; in practice, a combination of buffer zones and partial saturation often balances bias and power better than either alone.
Explain how users on edges between clusters can be exposed to both treatment and control conditions, leading to spillover effects that bias estimates.
Describe how creating buffer zones (geographic or network-based) between treatment and control clusters reduces spillover by excluding edge users from the analysis or by separating clusters physically.
Discuss how partial saturation (e.g., treating only a fraction of users within a cluster) can create a gradient of exposure, allowing estimation of spillover effects and reducing contamination.
Analyze the trade-offs: buffers reduce effective sample size and power, while partial saturation may introduce dilution of treatment effect; both require careful design to balance bias and precision.
Suggest that in practice, combining buffers with partial saturation or using network-based cluster detection can mitigate edge effects while maintaining sufficient power.
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Time spent is such a seductive metric and I've seen it get gamed badly.
Start by clarifying the feature's goal and the company's north star, then propose a primary metric that directly measures user value delivered, along with two guardrail metrics that protect against negative side effects. Critically evaluate 'time spent per user per day' by discussing its alignment with goals, potential for manipulation, and propose a better alternative like 'meaningful interactions per user' or 'retention rate'.
Pro tip: Always tie metrics to the feature's specific objective and the company's overall mission; avoid generic metrics without context. Show awareness that time spent can be a vanity metric if it doesn't reflect genuine user value.
Ask clarifying questions to understand what the feature does, its intended user impact, and how it aligns with Meta's mission and business objectives.
Select a primary metric that directly measures the feature's success in delivering value to users, such as meaningful interactions or retention.
Choose two guardrail metrics that monitor potential negative consequences, such as user well-being, content quality, or system performance.
Discuss pros (e.g., easy to measure, correlates with engagement), cons (e.g., may not indicate value, can incentivize addictive behavior), and manipulation risks (e.g., autoplay, clickbait).
Suggest at least one alternative primary metric that better captures user value, such as 'meaningful social interactions' or 'daily active users completing core action'.
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Start by stating the design effect formula for cluster randomization: DE = 1 + (m - 1) * ICC, where m is the average cluster size. Then plug in the given values to compute DE for the first scenario, and for the second scenario recalculate with the new m and ICC, comparing the results to explain how DE changes.
Pro tip: Emphasize that the design effect quantifies the variance inflation due to clustering, and that even if ICC halves, doubling cluster size can still increase DE because the (m-1) term grows. This shows you understand the trade-off between cluster size and intra-cluster correlation.
Recall and write the formula for design effect in cluster randomized trials: DE = 1 + (m - 1) * ICC, where m is the average cluster size and ICC is the intraclass correlation coefficient.
Plug in m = 20 and ICC = 0.05 into the formula: DE = 1 + (20 - 1) * 0.05 = 1 + 19 * 0.05 = 1.95.
For doubled cluster size (m = 40) and halved ICC (ICC = 0.025), compute DE = 1 + (40 - 1) * 0.025 = 1 + 39 * 0.025 = 1.975.
Compare the two design effects: 1.95 vs. 1.975. The design effect slightly increases, meaning that despite the ICC halving, the larger cluster size leads to a net increase in variance inflation. Discuss implications for sample size and power.
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Positive spillovers bias the naive estimate downward because control users get some benefit from being connected to treated users, shrinking the apparent gap.
First, clarify that the naive estimate is the difference in means between treated and control individuals, which ignores interference. Then, analyze how spillovers from treated to control units and within treatment arms bias this estimate, considering the sign and magnitude of spillovers. Finally, provide real-world examples of positive and negative spillovers to illustrate the intuition.
Pro tip: Emphasize that the direction of bias depends on the relative strength of spillovers to control versus treatment units, and that even with positive spillovers, the naive estimate can be biased downward if control units benefit more.
State that the naive estimator compares outcomes of treated and control individuals, assuming no interference (SUTVA). Explain that with spillovers, this assumption is violated.
Consider how treated units affect control units. If spillovers are positive, control outcomes increase, reducing the observed difference (bias downward). If negative, control outcomes decrease, increasing the difference (bias upward).
Consider how treated units affect each other. Positive spillovers among treated units increase treated outcomes, increasing the difference (bias upward). Negative spillovers decrease treated outcomes, reducing the difference (bias downward).
The net bias depends on the sum of spillover effects on control and treatment units. If positive spillovers to control outweigh those to treatment, bias is downward; if the reverse, bias is upward. Similarly for negative spillovers.
Give examples: Positive spillover: vaccination (treated individuals protect controls). Negative spillover: ride-sharing (treated drivers reduce demand for control drivers).
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