Explain regression discontinuity with an example

Last updated: January 8, 2026

Quick Overview

Explain regression discontinuity in simple terms and provide a concrete example.

Plaid
Analytics & Experimentation
Data Scientist
Plaid
January 8, 2026
Data Scientist
Onsite
Analytics & Experimentation
Hard

119

7

1,688 solved


Explain regression discontinuity in simple terms and provide a concrete example.

This analytics question from Plaid'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
Novelty and primacy effects
A/B testing methodology
Metric definition and success criteria
Long-term vs short-term metrics
Sample size and power calculation
How to Approach This
  1. Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
  2. Run experiments long enough to account for novelty effects and weekly seasonality.
  3. Use funnel analysis to identify where users drop off for maximum optimization impact.
  4. Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
  5. Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
  • How would you handle seasonality in your experiment?
  • 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?
  • What would you do if a stakeholder wants to end the experiment early because initial results look good?
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