Efficient Causal Discovery for Autoregressive Time Series

TL;DR

This paper introduces a new constraint-based algorithm for causal discovery in nonlinear autoregressive time series, offering improved efficiency and scalability, especially with limited data, outperforming existing methods.

Contribution

The paper presents a novel causal discovery algorithm tailored for nonlinear autoregressive time series that reduces computational complexity and enhances performance.

Findings

Outperforms existing causal discovery methods on synthetic data.

Maintains high accuracy with limited data.

Demonstrates scalability to larger problems.

Abstract

In this study, we present a novel constraint-based algorithm for causal structure learning specifically designed for nonlinear autoregressive time series. Our algorithm significantly reduces computational complexity compared to existing methods, making it more efficient and scalable to larger problems. We rigorously evaluate its performance on synthetic datasets, demonstrating that our algorithm not only outperforms current techniques, but also excels in scenarios with limited data availability. These results highlight its potential for practical applications in fields requiring efficient and accurate causal inference from nonlinear time series data.