##### 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**.