Bayesian networks and causal graphs
Bayesian networks discrib statistical models (no causality):
With local markov assumption, we can do bayesian network factorization:
Minimality assumption:
Causal edge assumption:
In a directed graph, every parent is a direct cause of all its children.
The Blocks in Graphs
Here we define the “blocks” in graphs, where the blocked path means $X$ and $Y$ are independent.
The proof is easy in chains:
However, things are different in immoralities, including its descentants:
Here we define the “blocked path”:
And then we define “d-separation” based on the blocked path:
We can distinguish causal association and confounding asscociation with causal egde assumption:
