Explain confounding with an Uber example

Question

In the context of analyzing Uber/Uber Eats data, explain what a confounding effect is.

1. Define confounder and why it can bias an observed relationship.
2. Give a concrete Uber-related example (avoid generic examples like age/sex/demographics).
3. Describe at least two practical ways you would detect and/or mitigate confounding in an analysis.

Expectations

• Your example should clearly identify:
  • the treatment/exposure (X)
  • the outcome (Y)
  • the confounder (Z) that affects both X and Y
• Explain the direction of the bias intuitively (how it could create a false effect or hide a real one).