Structural & Granger CAUSALITY for IoT Digital Twin

TL;DR

This paper introduces a method for estimating causality in IoT sensor data using Structural Vector Autoregressive models, Kalman Filter, and ICA, resulting in a Causal Digital Twin for various industries.

Contribution

It establishes the theory and algorithms for causality analysis in IoT, creating a versatile Causal Digital Twin applicable across multiple sectors.

Findings

Successful estimation of causality factors from sensor data

Demonstration on NASA bearing data

Potential for counterfactual experiments

Abstract

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.