# Detecting causal associations in large nonlinear time series datasets

## TL;DR

This paper presents a new method for detecting causal relationships in large, high-dimensional, nonlinear time series data, improving accuracy over existing techniques and applicable across various scientific fields.

## Contribution

The authors introduce a novel causal discovery approach combining flexible independence tests with a scalable algorithm for large-scale time series datasets.

## Key findings

- Outperforms existing methods in detecting causal links.
- Effective on both small and large datasets.
- Validated on climate and synthetic data.

## Abstract

Identifying causal relationships from observational time series data is a key problem in disciplines such as climate science or neuroscience, where experiments are often not possible. Data-driven causal inference is challenging since datasets are often high-dimensional and nonlinear with limited sample sizes. Here we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm that allows to reconstruct causal networks from large-scale time series datasets. We validate the method on a well-established climatic teleconnection connecting the tropical Pacific with extra-tropical temperatures and using large-scale synthetic datasets mimicking the typical properties of real data. The experiments demonstrate that our method outperforms alternative techniques in detection power from small to large-scale datasets and opens up entirely new possibilities to discover causal networks from time series across a range of research fields.