Summary
The concepts of
- Ideal manipulation
- Causal Markov and faithfulness assumptions
enable us to use Bayesian networks as causal graphs for causal reasoning and causal discovery
Under certain conditions and assumptions, we can discover causal relationships from observational data
The constraint-based and Bayesian approaches have different strengths and weaknesses