  In this foundational expository article on the application of Causality
Analysis in IoT, we establish the basic theory and algorithms for estimating
Structural and Granger causality factors from measured multichannel sensor data
(vector timeseries). Vector timeseries is modeled as a Structural Vector
Autoregressive (SVAR) model; utilizing Kalman Filter and Independent Component
Analysis (ICA) methods, Structural and generalized Granger causality factors
are estimated. The estimated causal factors are presented as a Fence graph
which we call Causal Digital Twin. Practical applications of Causal Digital
Twin are demonstrated on NASA Prognostic Data Repository Bearing data
collection. Use of Causal Digital Twin for counterfactual experiments are
indicated.
  Causal Digital Twin is a horizontal solution that applies to diverse use
cases in multiple industries such as Industrial, Manufacturing, Automotive,
Consumer, Building and Smart City.
