Jupyter notebooks for practical sessions

1a) Introduction to scikit-learn

• Introduction to scikit-learn:

1b) Statistical learning and regularized linear models

• Common pitfalls in the interpretation of coefficients of linear models:

2a) Hyper-parameters selection for flexible models

• Hyper-parameters selection for flexible models:

2b) Choice of learners for double machine learning

• Choice of learners for double machine learning:

3a) Introduction to Directed Acyclic Graphs (DAGs)

• DAGs, valid and invalid adjustment sets:

3b) Introduction to DoubleML

• Double ML Partial linear regression model (PLR), pratical example with the pension datasets and various sets of confounders:

4) Event studies: Causal methods for pannel data

• Comparison of different methods for pannel data: