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

• URL: http://arxiv.org/abs/2104.05828v1
• Date: Mon, 12 Apr 2021 21:30:53 GMT
• Title: Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm
• Authors: PG Madhavan
• Abstract summary: We describe the basics of Causality and Causal Graphs and develop a Learning Causal Digital Twin (LCDT) solution. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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