Syllabus

Our intensive two-day training provides an introduction to Causal Machine Learning. Preliminary knowledge of either one of the two fields of Causal Inference or (predictive) Machine Learning are helpful, but not required. To make the most out of the course, we recommend participants to have previous experience of programming in Python. Before the course starts, we will send some “Getting Started” information to participants so they are ready to start the training. In case you have questions, please don’t hesitate to contact .

Day 1: Basics of Causal Machine Learning

  • Basics of Causal Inference & Causal ML
    • Potential Outcomes
    • Directed Acyclic Graphs
    • Challenges of Machine-Learning based Causal Inference
  • Introduction to DoubleML
    • Key ingredients of DoubleML
  • Basic Causal Models
    • Partially Linear Regression
    • Interactive Regression Model
    • IV Models
  • Heterogeneous Treatment Effects
    • Group Average Treatment Effects (GATEs)
    • Conditional Average Treatment Effects (CATEs)
  • Hands-on session (Python) in small groups and Q&A

References

Chernozhukov et al. (2018)

Bach et al. (2022)

Bach et al. (2021)

Semenova and Chernozhukov (2021)

Day 2: Advanced Topics

  • Sensitivity Analysis with DoubleML

  • Causal Models and Extensions

    • DoubleML for Difference-in-Differences

  • Outlook and Recent Developments

  • Industry Use-Case Session

  • Hands-on session (Python) in small groups and Q&A

References

Chernozhukov et al. (2022)

Chang (2020)

Zimmert (2020)

Sant’Anna and Zhao (2020)

References

Bach, Philipp, Victor Chernozhukov, Sven Klaassen, Malte S. Kurz, and Martin Spindler. 2021. “DoubleML – An Object-Oriented Implementation of Double Machine Learning in R.” https://arxiv.org/abs/2103.09603.
Bach, Philipp, Victor Chernozhukov, Malte S. Kurz, and Martin Spindler. 2022. “DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python.” Journal of Machine Learning Research 23 (53): 1–6. http://jmlr.org/papers/v23/21-0862.html.
Chang, Neng-Chieh. 2020. “Double/debiased machine learning for difference-in-differences models.” The Econometrics Journal 23 (2): 177–91. https://doi.org/10.1093/ectj/utaa001.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/debiased machine learning for treatment and structural parameters.” The Econometrics Journal 21 (1): C1–68. https://doi.org/10.1111/ectj.12097.
Chernozhukov, Victor, Carlos Cinelli, Whitney Newey, Amit Sharma, and Vasilis Syrgkanis. 2022. “Long Story Short: Omitted Variable Bias in Causal Machine Learning.” National Bureau of Economic Research.
Sant’Anna, Pedro H. C., and Jun Zhao. 2020. “Doubly Robust Difference-in-Differences Estimators.” Journal of Econometrics 219 (1): 101–22. https://doi.org/https://doi.org/10.1016/j.jeconom.2020.06.003.
Semenova, Vira, and Victor Chernozhukov. 2021. “Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions.” The Econometrics Journal 24 (2): 264–89.
Zimmert, Michael. 2020. “Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding.” https://arxiv.org/abs/1809.01643.