# Graphs in State-Space Models for Granger Causality in Climate Science

arXiv: https://arxiv.org/abs/2307.10703 · HTML: https://mlsanity.com/paper/2307.10703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 20 Jul 2023.

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

- [GraphEM: EM algorithm for blind Kalman filtering under graphical sparsity constraints](https://mlsanity.com/paper/2001.03195/) (2020)

## Abstract (arXiv)

Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of state-space models. For that, we use GraphEM, a recently presented expectation-maximisation algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularisation is included in the M-step, which is solved using a proximal splitting Douglas-Rachford algorithm. Experiments in toy examples and challenging climate problems illustrate the benefits of the proposed model and inference technique over standard Granger causality methods.