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Measure whether posts strengthen friendships

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competencies in causal inference, metric definition, experimental design, bias identification, and segmentation within social network analytics.

  • Hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Measure whether posts strengthen friendships

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: Hard

Interview Round: Technical Screen

## Product question You are a Data Scientist on a social network. A stakeholder asks: **“Do posts help strengthen relationships between friends?”** ## Task Describe how you would **measure** whether posts strengthen friend relationships. Your answer should cover: 1. **Define “relationship strength”** (propose multiple candidate metrics and clarify tradeoffs). 2. A **measurement strategy** (observational analysis and/or experiment). 3. Key **confounders/biases** (e.g., homophily, activity level, feed ranking, reverse causality) and how you would mitigate them. 4. **Segmentation** you would use (e.g., new vs old friendships, weak vs strong ties). 5. What would convince you the feature is helping vs merely correlating with strong friendships already.

Quick Answer: This question evaluates a data scientist's competencies in causal inference, metric definition, experimental design, bias identification, and segmentation within social network analytics.

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Jul 28, 2025, 12:00 AM
Data Scientist
Technical Screen
Analytics & Experimentation
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Product question

You are a Data Scientist on a social network.

A stakeholder asks: “Do posts help strengthen relationships between friends?”

Task

Describe how you would measure whether posts strengthen friend relationships.

Your answer should cover:

  1. Define “relationship strength” (propose multiple candidate metrics and clarify tradeoffs).
  2. A measurement strategy (observational analysis and/or experiment).
  3. Key confounders/biases (e.g., homophily, activity level, feed ranking, reverse causality) and how you would mitigate them.
  4. Segmentation you would use (e.g., new vs old friendships, weak vs strong ties).
  5. What would convince you the feature is helping vs merely correlating with strong friendships already.

Solution

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