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Diagnose sales correlations without claiming causality

MetaData Scientist · Technical Screen
HardData ScientistTechnical ScreenAnalytics & Experimentation

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This question tests the ability to design an observational study that identifies associations between a new feature adoption and sales growth while strictly avoiding causal claims in the presence of self-selection bias.

Scenario

A product team at Meta has launched a new 'Quick-Buy' button in the Marketplace. Initial data shows that users who click the 'Quick-Buy' button spend 20% more on average than those who do not. The product manager wants to report this as a 'driver of revenue growth' to stakeholders.

Task

Design an analytical framework to quantify the relationship between 'Quick-Buy' usage and sales volume without assuming causality. Your response must address:

  1. Cohort Construction: How will you define the treatment and control groups to ensure temporal alignment (e.g., handling the exposure window)?
  2. Confounding Control: Identify at least three potential confounding variables (e.g., user tenure, device type, or historical shopping frequency) and explain how you would adjust for them in your analysis.
  3. Bias Mitigation: Propose a method to test if the observed effect is driven by 'power users' who were already predisposed to higher spending.
  4. Sensitivity/Robustness: Describe a check to determine if the correlation holds when you lag the independent variable.
  5. Communication: How would you frame the results in a report to stakeholders to ensure they do not misinterpret the correlation as a direct causal impact of the feature?

Constraints

  • Assume you have access to a user-level dataset containing user_id, timestamp, event_type (button click vs. non-click), daily_spend, and user metadata.
  • Focus on the methodology of quantifying the association rather than building a machine learning model.

Hints

Hint 1

Consider using a 'Difference-in-Differences' or 'Fixed-Effects' approach to account for time-invariant characteristics of the users.

Hint 2

Address selection bias by comparing spending patterns before the feature was ever introduced to the platform.

Hint 3

Focus on how you would articulate the 'selection effect'—the idea that users who value the product more are more likely to adopt the feature.

Follow-up questions

  • How would you design a Randomized Controlled Trial (RCT) to confirm if this feature actually drives additional spend?
  • If the feature is available globally and cannot be randomized, what quasi-experimental methods could you use to improve the causal inference?
  • What metrics would you monitor to ensure that an increase in 'Quick-Buy' usage is not cannibalizing other revenue streams?