Graphical Markov models, unifying results and their interpretation

TL;DR

This paper reviews the development and interpretation of Graphical Markov models, especially regression graph models, highlighting their ability to trace developmental pathways and predict structures in complex data.

Contribution

It unifies results on Graphical Markov models and demonstrates how regression graph models can analyze variable dependencies and causal pathways in longitudinal studies.

Findings

Regression graph models enable analysis of variable dependencies.

Models can predict effects of variable omission or subpopulation selection.

Illustrated with examples demonstrating model properties.

Abstract

Graphical Markov models combine conditional independence constraints with graphical representations of stepwise data generating processes.The models started to be formulated about 40 years ago and vigorous development is ongoing. Longitudinal observational studies as well as intervention studies are best modeled via a subclass called regression graph models and, especially traceable regressions. Regression graphs include two types of undirected graph and directed acyclic graphs in ordered sequences of joint responses. Response components may correspond to discrete or continuous random variables and may depend exclusively on variables which have been generated earlier. These aspects are essential when causal hypothesis are the motivation for the planning of empirical studies. To turn the graphs into useful tools for tracing developmental pathways and for predicting structure in alternative models, the generated distributions have to mimic some properties of joint Gaussian distributions. Here, relevant results concerning these aspects are spelled out and illustrated by examples. With regression graph models, it becomes feasible, for the first time, to derive structural effects of (1) ignoring some of the variables, of (2) selecting subpopulations via fixed levels of some other variables or of (3) changing the order in which the variables might get generated. Thus, the most important future applications of these models will aim at the best possible integration of knowledge from related studies.