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A Bayesian network is a probabilistic directed acyclic graph. Nodes represent random variables in the Bayesian sense (observable or unobservable); edges represent conditional dependencies between nodes.
9
votes
1
answer
918
views
Causality: Models, Reasoning and Inference, by Judea Pearl: Causal Bayesian Networks and the...
Background:
$\newcommand{\doop}{\operatorname{do}}\newcommand{\op}[1]{\operatorname{#1}}$
Definition 1.2.2 (Markov Compatibility) If a probability function $P$ admits the factorization of
$$P(x_1,\d …
2
votes
Accepted
What justifies the multiplication step in the proof of the front-door adjustment?
$\newcommand{\doop}{\operatorname{do}}$It's a bit more complicated than that. As outlined in Causal Inference in Statistics: A Primer, by Pearl, Glymour, and Jewell, on p. 68, we follow this line of r …
5
votes
Accepted
Causal Diagram and multiple regression
Your interpretation is correct. Conditioning on $A$ blocks the backdoor path $B\leftarrow A\to C\to D.$ Since $C$ is unavailable because it is unmeasured, you must condition on $A$ to block the backdo …