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.