Structural & Granger CAUSALITY for IoT Digital Twin
This provides a foundational framework for causality analysis in IoT, applicable across multiple industries, but it is incremental as it builds on existing SVAR, Kalman Filter, and ICA methods.
The paper tackles the problem of estimating causal relationships from IoT sensor data by developing a Causal Digital Twin using Structural and Granger causality methods, with results demonstrated on NASA bearing data for applications like counterfactual experiments.
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.