Pairwise Markov properties for regression graphs

TL;DR

This paper explores the different pairwise Markov properties in regression graphs generated from sequences of regressions, establishing their equivalence to the global Markov property under certain conditions.

Contribution

It introduces and compares various pairwise Markov properties for regression graphs, proving their equivalence to the global Markov property for compositional graphoids.

Findings

Multiple pairwise Markov properties are equivalent for regression graphs.

All pairwise properties are equivalent to the global Markov property.

The paper provides theoretical foundations for interpreting dependencies in regression graphs.

Abstract

With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of conditional distributions, named regressions in joint responses. The involved random variables may be discrete, continuous or of both types. Such a generating process specifies for each response a conditioning set which contains just its regressor variables and it leads to at least one valid ordering of all nodes in the corresponding regression graph which has three types of edge; one for undirected dependences among context variables, another for undirected dependences among joint responses and one for any directed dependence of a response on a regressor variable. For this regression graph, there are several definitions of pairwise Markov properties, where each interprets the conditional independence associated with a missing edge in the graph in a different way. We explain how these properties arise, prove their equivalence for compositional graphoids and point at the equivalence of each one of them to the global Markov property.