Explain confounding with an Uber example

Q: Explain confounding with an Uber example

This question evaluates a data scientist's understanding of confounding and causal inference in observational datasets by testing recognition of variables that bias observed associations between an exposure and an outcome.

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Question

Question

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

Define confounder and why it can bias an observed relationship.
Give a concrete Uber-related example (avoid generic examples like age/sex/demographics).
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).

Explain confounding with an Uber example

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Expectations

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