Interpretable Causal Graphical Models for Equilibrium Systems with Confounding

TL;DR

This paper develops interpretable graphical models for equilibrium systems with confounding, enabling better causal inference and adjustment set selection in complex equilibrium scenarios.

Contribution

It introduces a novel graphical modeling approach for equilibrium systems with confounding, incorporating counterfactual and observational variables using anterial graphs.

Findings

Valid graphical representations of counterfactual and observational variables.

A flexible procedure for selecting adjustment sets.

Application to equilibrium systems with confounding.

Abstract

In applications, quantities of interest are often modelled in equilibrium or an equilibrium solution is sought. The presence of confounding makes causal inference in this setting challenging. We provide interpretable graphical models for equilibrium systems with confounding using anterial graphs (Lauritzen and Sadeghi, 2018), a class of graphs containing directed acyclic graphs, ancestral graphs, and chain graphs. In this setting, we provide valid graphical representations of both counterfactual variables and observational variables, which we relate to counterfactual graphs (Shpitser and Pearl, 2007) and single-world intervention graphs (Richardson and Robins,2013). As an application of this graphical representation, we provide an element-wise procedure of selecting adjustment sets that flexibly include and exclude given covariates.