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

Structural & Granger CAUSALITY for IoT Digital Twin