Explain propensity score matching with an example
Last updated: April 8, 2026
Quick Overview
Explain propensity score matching in simple terms and provide a concrete example.
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Explain propensity score matching in simple terms and provide a concrete example.
Analytics questions at Reddit evaluate your ability to define metrics, design experiments, and derive actionable insights from data. This Phone Screen question tests your end-to-end analytical thinking.
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 seasonality in your experiment?
- How would you handle interference between treatment and control?
- How would you handle an experiment where the control and treatment groups are different sizes?
- What if you discover a bug in the logging during the experiment?
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