  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.
