Assess the causal impact of providing free shuttle buses on employee participation rates across 1,000+ company sites.

At what data grain (site-level vs. individual-level) would you run the analysis and why? Start with an OLS specification: write the regression equation, list key controls, and explain how to interpret the shuttle-service coefficient. What limitations does basic OLS have in this context and how would a Difference-in-Differences (DiD) design address them? Design a placebo test to check the DiD identifying assumption. If DiD assumptions fail, outline how you would apply Propensity Score Matching (PSM). Which variables would you include and why? Give a high-level intuition for why Lasso regression performs effective feature selection when estimating propensity scores. Discuss trade-offs of matching with vs. without replacement and how you would run balance checks. After matching, what is the next analytical step (e.g., PSM + DiD) and how would you report results to stakeholders?

Cover assumptions, diagnostics, coefficient interpretation, and stakeholder communication; reference causal inference best practices.

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