Explain propensity score matching with an example
Last updated: December 12, 2025
Quick Overview
Explain propensity score matching in simple terms and provide a concrete example.
Databricks
December 12, 2025270
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4,500 solved
Explain propensity score matching in simple terms and provide a concrete example.
This analytics question from Databricks's Onsite tests your ability to think critically about data. The interviewer expects you to consider confounding variables, selection bias, and the difference between correlation and causation.
What the Interviewer Expects
- Design complex experimentation strategies for tricky scenarios
- Handle multi-armed bandits, switchback experiments, and quasi-experiments
- Address long-term effects vs short-term metrics
- Propose causal inference methods when randomization is not possible
- Build a measurement framework that connects metrics to business value
- Discuss organizational experimentation culture and maturity
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
- How would you handle an experiment where the control and treatment groups are different sizes?
- How would you handle interference between treatment and control?
- What if you discover a bug in the logging during the experiment?
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