Bayesian Data Analysis





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Course Contents

Correlation vs Causality, Omitted Variables, Measurement Error, Randomized Experiments, Introduction to the course; Random Variable, Probability Review. Observational Studies and Regression, Overview of research design, and examples. Measurement Error and Omitted Variables Bias, Applications. Experimental Designs, Applications. Quasi Experimental Designs, Applications. Selection on Observables: Matching and Propensity Scores, Applications. Selection on Observables: Fixed Effects, Pre Post Designs and Difference in Differences, Applications. Regression Discontinuity, Applications. Instrumental Variables, Applications. Recent Developments in the field: Discussion of papers on new methods and applications.





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