Assess education–income effect credibly

Q: Assess education–income effect credibly

This is a Analytics & Experimentation interview question from Google for Data Scientist roles. View the full question and solution on PracHub.

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Question

You collected data on 1,000 Mountain View residents: College (binary; attended any college) and Income (annual). Is regressing Income on College alone appropriate for estimating the effect of college on income? Identify issues of external validity (Mountain View not representative), model choice (binary predictor in linear regression vs two-sample t test), and causal identification (confounding by age, occupation, parental income, ability). Propose a better analysis: specify the estimand (ATE of college on income), include covariates in a regression or matching framework, perform balance checks, and discuss alternative designs (instrumental variables, regression discontinuity, randomized encouragement). Explain how you would report uncertainty (confidence intervals) and sensitivity to unobserved confounding.

Assess education–income effect credibly

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