Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm

TL;DR

This paper introduces a real-time learning Causal Digital Twin (LCDT) that enhances prescriptive analytics by enabling cause-effect analysis and counterfactual simulations, simplifying deployment and improving operational decision-making.

Contribution

It develops a novel LCDT algorithm using a recurrent neural network with causal graph integration, enabling online learning with minimal setup for digital twin applications.

Findings

Successful real-world vibration data experiment

Accurate causal factor estimation achieved

Effective what-if and counterfactual analysis demonstrated

Abstract

Evidence-based Prescriptive Analytics (EbPA) is necessary to determine optimal operational set-points that will improve business productivity. EbPA results from what-if analysis and counterfactual experimentation on CAUSAL Digital Twins (CDTs) that quantify cause-effect relationships in the DYNAMICS of a system of connected assets. We describe the basics of Causality and Causal Graphs and develop a Learning Causal Digital Twin (LCDT) solution; our algorithm uses a simple recurrent neural network with some innovative modifications incorporating Causal Graph simulation. Since LCDT is a learning digital twin where parameters are learned online in real-time with minimal pre-configuration, the work of deploying digital twins will be significantly simplified. A proof-of-principle of LCDT was conducted using real vibration data from a system of bearings; results of causal factor estimation, what-if analysis study and counterfactual experiment are very encouraging.