Chain graph models and their causal interpretations (with discussion).

*(English)*Zbl 1090.62103Summary: Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs. However, the apparent simplicity of chain graphs belies the subtlety of the conditional independence hypotheses that they represent. There are many simple and apparently plausible, but ultimately fallacious, interpretations of chain graphs that are often invoked, implicitly or explicitly. These interpretations also lead to flawed methods for applying background knowledge to model selection. We present a valid interpretation by showing how the distribution corresponding to a chain graph may be generated from the equilibrium distributions of dynamic models with feed-back. These dynamic interpretations lead to a simple theory of intervention, extending the theory developed for directed acyclic graphs. Finally, we contrast chain graph models under this interpretation with simultaneous equation models which have traditionally been used to model feed-back in econometrics.

##### MSC:

62M99 | Inference from stochastic processes |

05C90 | Applications of graph theory |

05C20 | Directed graphs (digraphs), tournaments |

##### Keywords:

causal model; feedback system; Gibbs sampler; intervention theory; structural equation model
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\textit{S. L. Lauritzen} and \textit{T. S. Richardson}, J. R. Stat. Soc., Ser. B, Stat. Methodol. 64, No. 3, 321--361 (2002; Zbl 1090.62103)

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