Explain propensity score matching with an example
Last updated: January 12, 2026
Quick Overview
Explain propensity score matching in simple terms and provide a concrete example.
Workday
January 12, 20269
5
4,482 solved
Explain propensity score matching in simple terms and provide a concrete example.
Analytics questions at Workday evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Technical Screen question tests your end-to-end analytical thinking.
What the Interviewer Expects
- Design a rigorous experiment with proper randomization and sample size calculation
- Define primary and guardrail metrics with clear rationale
- Address novelty effects, network effects, and interference
- Segment results appropriately and identify heterogeneous treatment effects
- Propose follow-up analyses when results are ambiguous
Key Topics to Cover
How to Approach This
- Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
- Run experiments long enough to account for novelty effects and weekly seasonality.
- Use funnel analysis to identify where users drop off for maximum optimization impact.
- Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
- Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
- What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
- What if you discover a bug in the logging during the experiment?
- How would you handle an experiment where the control and treatment groups are different sizes?
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Browse Analytics QuestionsSample Answer
Problem Setup
The analytical question we want to answer is: "How does the implementation of Workday's new performance management feature affect employee productivity?" We need data on employee productivity metrics ...
Methodology
To assess the impact of the new feature while controlling for confounding variables, we will use propensity score matching (PSM). This involves estimating the probability (propensity score) that an em...
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