Explain propensity score matching with an example

Last updated: July 25, 2025

Quick Overview

Explain propensity score matching in simple terms and provide a concrete example.

Bloomberg
Statistics & Math
Data Scientist
Bloomberg
July 25, 2025
Data Scientist
Take-home Project
Statistics & Math
Hard

17

5

4,473 solved


Explain propensity score matching in simple terms and provide a concrete example.

This statistics question from Bloomberg's Take-home Project tests your ability to apply mathematical reasoning to practical problems. The interviewer expects precise definitions, correct methodology, and awareness of assumptions and limitations.

What the Interviewer Expects
  • Derive results from first principles when needed
  • Handle complex scenarios with multiple interacting variables
  • Design experiments that account for real-world complications
  • Discuss advanced topics: Bayesian methods, causal inference, resampling
  • Connect statistical concepts to business decision-making
  • Identify subtle errors in reasoning (Simpson's paradox, survivorship bias)
Key Topics to Cover
Conditional probability and Bayes theorem
Central Limit Theorem
Hypothesis testing (H0, H1, p-values)
Probability distributions
Bayesian vs frequentist inference
Causal inference basics
How to Approach This
  1. Define your hypotheses (H0 and H1) clearly before performing any test.
  2. Calculate required sample size BEFORE running an experiment, using power analysis.
  3. Remember the Central Limit Theorem: sample means become approximately normal with large n.
  4. Watch for Simpson's paradox. Always segment data by key dimensions.
  5. Distinguish between statistical significance and practical significance.
Possible Follow-up Questions
  • What if the sample size is very small?
  • What alternative statistical method could you use here?
  • How would you handle multiple comparisons?
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