Description
Analysis of data driven case studies and the associated estimation methods used to make causal statements in the context of business applications. Particual emphasis will be given to the causal questions associated to business applications in Economics, Finance, Management, Marketing, IT, HR, etc.
The aim of this course is to provide an advanced introduction to econometric causality and machine learning tools commonly used by researchers in business schools. Each session of the course starts from the study of a case study in Economics, Finance, Management, Marketing, IT, HR, etc. The applied nature of the course is such that it is given less attention to the theoretical demonstrations of the econometric tools used in the application. The analysis of the case study is used to get familiar with the estimation methods used to answer the main question of the case study. An important part of the course is devoted to the actual replication of the empirical analysis of the case study using Stata. Part of the course will cover applications of "program evaluation" in business contexts. Another portion of the course will cover some of the fundamental aspects of applications of machine learning in economics including treatment effects lasso estimators that are commonly used to provide causal estimates in a machine learning environment.
Thèmes couverts
- Causality and the experimental ideal
- Endogeneity
- Longitudinal methods
- Program and policy evaluation
- Machine Learning
- Business economics applications
- Management practices
- Treatment Effect
- Lasso estimation
- Random assignment
Difference-in-Difference
Remarques importantes
Course in French : MATH 80816
For Ph. D. students : There is no mandatory prerequisite course, but it is recommended to have an introductory econometrics course before attending 80816A.
Préalable(s) : MATH 60816(A)
