OT-SC-WS-08 | Causal learning
Prof. Dr. Vanessa Didelez
The course will give a basic introduction into the main concepts, fundamental assumptions and basic principles of key methods for causal learning.
We will cover terminology and central concepts such as potential outcomes, counterfactual prediction, causal diagrams, the target trial principle
- Basic methods for estimation, such as inverse-probability weighting, standardisation / g-formula, as well as checks, e.g. for balance and overlap
- A taste of causal random forests and double machine learning of causal effects
- Basic algorithms for causal discovery, such as the PC- and IDA-algorithms
At the end of the course, the participants will be able to employ potential outcomes notation and causal diagrams to represent prior causal knowledge, identify key structural assumptions as well as potential sources of bias (such as confounding or selection)
- Apply methods for learning (or estimating) causal effects of hypothetical interventions using the appropriate data analytic tools and assess the plausibility of required assumptions
- Apply basic alogrithms for causal discovery (structure learning) to real-world data, while appreciating their strengths and weaknesses.
A basic knowledge of statistical data analysis and familiarity with R will be helpful.
- Brain, coffee
- PC/laptop or similar
- For online format a second screen might be beneficial
- Didelez, V (2018). Chapter: Causal concepts and graphical models. In: Handbook of Graphical Models, 353-380, CRC-Press --- available from the author upon request
- Foraita, R, Friemel, J, Günther, K, Behrens, T, Bullerdiek, J, Nimzyk, R, Ahrens, W and Didelez, V (2020). Causal discovery of gene regulation with incomplete data. J. R. Stat. Soc. A, 183: 1747-1775. https://doi.org/10.1111/rssa.12565
- Witte, J, Didelez, V. (2019). Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal; 61: 1270– 1289. https://doi.org/10.1002/bimj.201700294
Book (free to download):
- Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.