Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm
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.
Keywords
Cite
@article{arxiv.2104.05828,
title = {Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm},
author = {PG Madhavan},
journal= {arXiv preprint arXiv:2104.05828},
year = {2021}
}