Units
1. Artificial Intelligence in Econometrics and Statistics: Roles, Boundaries, Authentication and Privacy 2. Data Cleaning and Preparation: Missing Data, Outliers, and Coding 3. Exploratory Data Analysis and Visualization: Graphing and Interpretation with Artificial Intelligence 4. Linear Regression: Model Building, Coefficient Interpretation and Assumptions 5. Regression Diagnostics and Violations: Collinearity, Heteroscedasticity, Autocorrelation 6. Hypothesis Testing and p-value: Significance, Power, and Multiple Comparisons 7. p-hacking, HARKing and Statistical Integrity: Pitfalls in the Age of Artificial Intelligence 8. Time Series Analysis: Stationarity, ARIMA and Forecasting 9. Causation and Policy Analysis: DiD, IV, RDD and Artificial Intelligence 10. Code Generation and Reproducibility: R/Python, Versioning and Reproducibility 11. Report Writing, Communication, and Ethics: Presenting Results and Communicating Boundaries
Unit 9 / 11

Causation and Policy Analysis: DiD, IV, RDD and Artificial Intelligence

Gains:

  • Understanding the distinction between correlation and causality and the logic of designs such as difference-in-difference (DiD), instrumental variable (IV), regression discontinuity (RDD) with the support of artificial intelligence
  • Being able to see the defining assumption on which each design is based (parallel trends, exclusion constraint, continuity in discontinuity) and the role of AI in producing test/defense code
  • Ability to critically evaluate the strategy of recognizing and identifying interpretations of artificial intelligence that produce excessive causal claims from observational data.
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