Explain causal inference with an example

Last updated: November 4, 2025

Quick Overview

Explain causal inference in simple terms and provide a concrete example.

Workday
Statistics & Math
Data Scientist
Workday
November 4, 2025
Data Scientist
Technical Screen
Statistics & Math
Easy

2

4

1,212 solved


Explain causal inference in simple terms and provide a concrete example.

Statistics questions at Workday test your ability to reason quantitatively and design rigorous experiments. This Technical Screen question evaluates your understanding of statistical inference and its application to business decisions.

What the Interviewer Expects
  • State the correct formula or theorem with clear definitions
  • Apply the concept to the given scenario step by step
  • Interpret the result in plain language
  • Identify assumptions and when they might be violated
Key Topics to Cover
Regression analysis
Central Limit Theorem
Conditional probability and Bayes theorem
Confidence intervals and significance levels
Multiple testing correction (Bonferroni, FDR)
Bayesian vs frequentist inference
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 assumptions does this test make, and how would you validate them?
  • What if the sample size is very small?
  • How would you design a follow-up experiment based on these results?
  • How would you handle multiple comparisons?
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Sample Answer
Problem formulation

Causal inference is the process of determining whether a change in one variable (the treatment or intervention) directly causes a change in another variable (the outcome). In a business context, under...

Solution approach

To analyze causal effects using regression analysis, we can start by collecting data on customers who have and have not used the new feature. The steps include:

  1. Data Collection: Gather data on ...

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