Explain confounding with an Uber example
Company: PayPal
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Onsite
##### Question
You are interviewing for a Data Scientist role and are given access to Uber / Uber Eats data. Answer the following about confounding in causal inference:
1. **Define confounding** in the context of estimating causal effects from observational data. Explain what a **confounder** is and *why* it can bias an observed relationship between an exposure and an outcome.
2. Give a **concrete Uber-related example** (avoid generic demographic examples like age/sex). Your example should clearly identify:
- the **treatment / exposure** (X),
- the **outcome** (Y), and
- the **confounder** (Z) that affects *both* X and Y.
Explain intuitively the **direction of the bias** (how it could manufacture a false effect or hide a real one).
3. Describe **at least two** practical ways you would **detect and/or mitigate** confounding in an analysis (in the design or the modeling), and state **what assumptions each method requires**.
Quick Answer: This Statistics & Math interview question for a Data Scientist tests understanding of confounding and causal inference in observational data. Candidates must define a confounder, give a concrete non-demographic Uber example identifying the exposure, outcome, and confounder with the direction of bias, and describe at least two mitigation methods along with the assumptions each requires.