Hierarchical Causal Analysis of Natural Languages on a Chain Event Graph

TL;DR

This paper introduces a hierarchical causal analysis framework using chain event graphs to better model interventions and causal discovery in reliability systems from natural language maintenance logs.

Contribution

It develops a novel tree-based causal model that enhances intervention calculus and automates causal discovery from natural language data in reliability contexts.

Findings

Supports typical reliability interventions with a bespoke causal calculus

Automates causal discovery from maintenance logs

Enables predictive inference on remedial interventions

Abstract

Various graphical models are widely used in reliability to provide a qualitative description of domain experts hypotheses about how a system might fail. Here we argue that the semantics developed within standard causal Bayesian networks are not rich enough to fully capture the intervention calculus needed for this domain and a more tree-based approach is necessary. We instead construct a Bayesian hierarchical model with a chain event graph at its lowest level so that typical interventions made in reliability models are supported by a bespoke causal calculus. We then demonstrate how we can use this framework to automate the process of causal discovery from maintenance logs, extracting natural language information describing hypothesised causes of failures. Through our customised causal algebra we are then able to make predictive inferences about the effects of a variety of types of remedial interventions. The proposed methodology is illustrated throughout with examples drawn from real maintenance logs.