Course content

• Problem of Causal Inference and Randomized Experiments: Cause and effects, randomized and non-randomized trials, treatment effect and selection bias
• Regression Fundamentals and Causality: Introduction to regression analysis, ordinary least squares regression, linear regression and conditional expectation, inference and hypothesis testing in regression, regression and causality
• Matching: Counterfactuals, propensity score matching, average treatment effect, quantile treatment effect, regression and matching, ordered and continuous treatment
• Instrumental Variables Regression: Instrumental variables and causality, Two-stage least squares, 2SLS inference, IV with heterogeneous potential outcomes
• Difference-in-difference Estimator: Individual fixed effects, difference-in-difference, regression DD

References

• Angrist J.D. and J. Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press.
• Cameron A.C. and P.K.Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge University Press. New York.
• Wooldridge J.M. 2010. Econometric Analysis of Cross Section and Panel Data (2nd Ed.). The MIT Press. Massachusetts.
• Heckman J.J. and E.J.Vytlacil. 2007. Chapter 70 Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation. Handbook of Econometrics. Vol.6, Part B. pp. 4779-4874
• Heckman J.J. and E.J.Vytlacil. 2007. Chapter 71 Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast Their Effects in New Environments. Handbook of Econometrics. Vol.6, Part B. pp. 4875-5143