Teaching

Course material

Causal Machine Learning (consolidated material of courses taught at Universities Aarhus, Hamburg, Luzern and Tübingen)

Causal Christmas Tree Challenge

The Challenge

Solutions 2022

Student shinyapps

Check out the Causal Forest Fun shinyapp my Tübinger Master students prepared as group assignment.

Simulation notebooks

These R Notebooks illustrate concepts and methods with simulated data. You can extract the code by clicking on the top right button and download the Rmd file to run/modify the code.

Basics:

• Basics: Convergence rates
• Basics: (Conditional) Independence
• Basics: Influence functions explained using OLS

Supervised ML:

• Supervised ML: Overfitting of OLS and value of training vs. test sample
• Supervised ML: Lasso saves the job of OLS
• Supervised ML: Tree-based methods

Causal ML:

• Causal ML: Why naive model selection fails
• Causal ML: Double Selection
• Causal ML: Partially linear Double ML
• Causal ML: AIPW Double ML (ATE)
• Causal ML: Group Average Treatment Effects
• Causal ML: Causal Tree and Causal Forest
• Causal ML: Meta-learner
• Causal ML: Offline policy learning
• Causal ML: Multi-armed bandit

Application notebooks

These R notebooks use the pension dataset of Chernozhukov and Hansen (2004) that is part of the hdm package. Focus is on hand-coding the methods to see how they work.

• Basics: OLS and Frisch-Waugh
• Supervised ML: Lasso
• Supervised ML: Tree-based methods
• Causal ML: Double Selection and Partially Linear Double ML
• Causal ML: Double ML for average treatment effects
• Causal ML: Double ML as generic recipe
• Causal ML: Double ML for group average treatment effects
• Causal ML: Predicting effects
• Causal ML: Offline policy learning

Identification notebooks

Showcase the use of DAGs and SWIGs.

• Causal Inference: DAG and SWIG for RCTs
• Causal Inference: DAG and SWIG for measured confounding