# Stochastic Formulation of Causal Digital Twin: Kalman Filter Algorithm

arXiv: https://arxiv.org/abs/2105.05236 · HTML: https://mlsanity.com/paper/2105.05236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 1 of 1)

- [Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm](https://mlsanity.com/paper/2104.05828/) (2021)

## Abstract (arXiv)

We provide some basic and sensible definitions of different types of digital twins and recommendations on when and how to use them. Following up on our recent publication of the Learning Causal Digital Twin, this article reports on a stochastic formulation and solution of the problem. Structural Vector Autoregressive Model (SVAR) for Causal estimation is recast as a state-space model. Kalman filter (and smoother) is then employed to estimate causal factors in a system of connected machine bearings. The previous neural network algorithm and Kalman Smoother produced very similar results; however, Kalman Filter/Smoother may show better performance for noisy data from industrial IoT sources.