### Discovering Causal Structure from Observations (Advanced Data Analysis from an Elementary Point of View)

How do we get our causal graph? Comparing rival DAGs by testing selected
conditional independence relations (or dependencies). Equivalence classes of
graphs. Causal arrows never go away no matter what you condition on ("no
causation without association"). The crucial difference between common causes
and common effects: conditioning on common causes makes their effects
independent, conditioning on common effects makes their causes dependent.
Identifying colliders, and using them to orient arrows. Inducing orientation
to enforce consistency. The SGS algorithm for discovering causal graphs; why
it works. The PC algorithm: the SGS algorithm for lazy people. What about
latent variables? Software: `TETRAD` and `pcalg`; examples of
working with `pcalg`. Limits to observational causal discovery:
universal consistency is possible (and achieved), but uniform consistency is
not.

*Reading*: Notes, chapter 24

Advanced Data Analysis from an Elementary Point of View

Posted at April 25, 2013 10:30 | permanent link