# Characterization of causal ancestral graphs for time series with latent confounders

arXiv: https://arxiv.org/abs/2112.08417 · HTML: https://mlsanity.com/paper/2112.08417/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2021.

## Built on by (most cited first, 6 of 6)

- [SpaceTime: Causal Discovery from Non-Stationary Time Series](https://mlsanity.com/paper/2501.10235/) (2025)
- [Unified Causality Analysis Based on the Degrees of Freedom](https://mlsanity.com/paper/2410.19469/) (2024)
- [Observational and Interventional Causal Learning for Regret-Minimizing Control](https://mlsanity.com/paper/2212.02435/) (2022)
- [Projecting infinite time series graphs to finite marginal graphs using number theory](https://mlsanity.com/paper/2310.05526/) (2023)
- [Using Time Structure to Estimate Causal Effects](https://mlsanity.com/paper/2504.11076/) (2025)
- [Learning Causal Structure of Time Series using Best Order Score Search](https://mlsanity.com/paper/2603.05370/) (2026)

## Abstract (arXiv)

In this paper, we introduce a novel class of graphical models for representing time lag specific causal relationships and independencies of multivariate time series with unobserved confounders. We completely characterize these graphs and show that they constitute proper subsets of the currently employed model classes. As we show, from the novel graphs one can thus draw stronger causal inferences -- without additional assumptions. We further introduce a graphical representation of Markov equivalence classes of the novel graphs. This graphical representation contains more causal knowledge than what current state-of-the-art causal discovery algorithms learn.