Business Applications of Data Analytics

Questions about cause are at the heart of many everyday questions and business decisions. Does eating an egg every day cause people to live longer or shorter or has no effect? Do gun control laws cause more or less murders or have no effect?
Does adding a new distribution center impact sales? Causal inference is the subfield of statistics that considers how we should make inferences about these type of questions.

Main Themes
This course will cover some of the key concepts and methods of causal inference.
Planned topics include the potential outcomes framework for causal inference, difference in differences, controlling for unmeasured confounders in observational studies, matching methodologies, sensitivity analysis for unmeasured confounding and tests of hidden bias. 

- Métodos Estadísticos Aplicados a Negocios
- Machine Learning
- Students should understand how to run and interpret a linear regression, including the concepts of standard errors, P-values, and confidence intervals.

SANTIAGO GALLINO. PhD in Operations and Information Management & Master’s in Statistics, University of Pennsylvania; MBA, IAE Business School; BS in Electrical Engineering, UBA. Professor at OID Department, Wharton School, University of Pennsylvania.