Causality

Knowledge:

• causal models versus observational models 
• observational distribution, intervention distribution, and counterfactuals
• graphical models and Markov conditions
• identifiability conditions for learning causal relations from observational and/or interventional data

Skills:

• working with graphs and graphical models 
• derivation of causal effects and predicting the result of interventional experiments
• performing variable adjustment for computing causal effects
• understanding of and ability to apply different methods for causal structure learning

Competences:

• causal reasoning
• learning causal structure from data