# Efficient Causal Discovery for Autoregressive Time Series

arXiv: https://arxiv.org/abs/2507.07898 · HTML: https://mlsanity.com/paper/2507.07898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 10 Jul 2025.

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

- [Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information](https://mlsanity.com/paper/1709.01447/) (2017)

## Built on by (most cited first, 2 of 2)

- [GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series](https://mlsanity.com/paper/2606.23880/) (2026)
- [LUCID: Learning Under Confounding for Inference and Discovery in Time Series](https://mlsanity.com/paper/2609.31315/) (2026)

## Abstract (arXiv)

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.