Applied Machine Learning & AI for Economics

Mandatory Pre-Readings

• James, G., Witten, D., Hastie, T. & Tibshirani, R. (2021). An Introduction to Statistical Learning. (Download ISL PDF). Read Chapter 1 and Chapter 2 (up to section 2.1.3).
• Cunningham, S. (2021). Causal Inference: The Mixtape. (Read Chapter 2 Online). Focus on 2.1–2.4, 2.7–2.17, 2.24–2.25.
• ISL, Chapter 3 (Sections 3.1–3.4 inclusive).

Questions

1. Prediction vs. Inference: Explain the fundamental difference between Prediction (forecasting a future outcome) and Inference (understanding the causal effect). Provide one economic example where pure prediction is sufficient, and one where causal inference is required.
2. Parametric vs. Non-parametric: Define "parametric" and "non-parametric" models within a statistical context. Why might an economist actively choose a rigid parametric model over a flexible non-parametric one?
3. Multiple Variable Regression: In a multivariate regression model, we interpret a coefficient as the effect of X "holding all other variables constant." At a high level, why does this interpretation become practically difficult when your dataset contains hundreds of overlapping variables?
4. ML in real life: Consider a real-world scenario where a firm uses a Machine Learning algorithm trained on historical data to automatically screen loan applications or job candidates. What are the economic or ethical risks of blindly deploying this model without understanding its internal logic?
5. OLS Geometry: Using (ISL chap 3.4), argue that in the case of simple linear regression, the least squares line always passes through the point (x̄, ȳ).