Machine Learning for Economists and Business Analysts
Machine learning estimation methods gain more and more popularity. Compared to conventional estimation methods, machine learning can solve statistical prediction tasks in a data adaptive way. Furthermore, machine learning can deal with high-dimensional variable spaces in a relatively flexible way. Prediction methods are used in many different business and economic domains. Examples of prediction tasks are: The prediction of sales for a grocery store, such that logisticians can ship products before they are sold. The prediction of the probability to become drug addicted later in life, such that drug prevention programs can be targeted at adolescents with high risk.
Besides predictions, economists and managers are often interested in causal questions. Examples of causal questions are: What are the effects of tweets by Elon Musk on Bitcoins? What impact has lowering the central bank interest rate on GDP? Does participation in training programs reduce the unemployment duration? Machine learning cannot give us an automatic answer to causal questions without using an empirical design. However, machine learning estimates can serve as input factors for these empirical designs. Furthermore, we can estimate heterogeneous effects with machine learning.
The course covers different predictive and causal machine learning methods. A focus will be on the application of these methods in practical R programming session.
Predictive Machine Learning:
- Regularized Regression
- Trees and Forests
- Unsupervised Machine Learning
Causal Machine Learning
- Double Selection Procedure
- Debiased Machine Learning
- Causal Forests
- Optimal Policy Learning
- Reinforcement Learning