This paper aims to solve an often noted incompatibility between graphical chain models which elucidate the conditional independence structure of a set of random variables and simultaneous equations systems which focus on direct linear interactions and correlations between random variables. Various authors have argued that the incompatibility arises mainly from the fact that in a simultaneous equations system (e.g., a LISREL model) reciprocal causality is possible whereas this is not so in the case of graphical chain models. In this article it is shown that this view is not correct. In fact, the definition of the Markov property embodied in a graph can be generalized to a wider class of graphs which includes certain nonrecursive graphs. The resulting class of reciprocal graph probability models strictly includes the class of chain graph probability models. The class of lattice conditional independence probability models is also strictly included. It is shown that the resulting methodology is directly applicable to quite general simultaneous equations systems that are subject to mild restrictions only. Provided some adjustments are made, general simultaneous equations systems can be handled as well. In all cases, consistency with the LISREL methodology is maintained.

Chain graph, Conditional independence, Finite distributive lattice, Gibbs factorization, Global Markov property, Graphical model, LISREL model, Nonrecursive causal model, Reciprocal graph, Simultaneous equations system, Undirected graph,
Annals of Statistics
Erasmus School of Social and Behavioural Sciences

Koster, J.T.A. (1996). Markov properties of nonrecursive causal models. Annals of Statistics, 24(5), 2148–2177. doi:10.1214/aos/1069362315