In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
At the usual time and place, there will be an exercise session, where Shu Li will be present to answer any question you have about the exercises in the script (and some additional ones).
- Peters, Janzing and Schölkopf (2017). Elements of Causality. MIT Press..
- Freedman, Pisani and Purves (2007). Statistics. Fourth edition. Chapters 1-2.
- Shalizi. Advanced Data Analysis from an Elementary Point of View. Draft available here. Chapters 20-25.
- Højsgaard, Edwards and Lauritzen (2012). Graphical Models with R.
- Pearl (2009). Causal inference in statistics: An overview. Available here.
- Spirtes, Glymour and Scheines (2000). Causation, Prediction and Search. MIT Press. Available here.
- Pearl (2009). Causality: Models, Reasoning and Inference. Wiley.
- Pearl, Glymour and Jewell (2016). Causal Inference in Statistics: A Primer. Wiley.
- Koller and Friedman (2009). Probabilistic Graphical Models. MIT Press. .