# Causal discovery for time series from multiple datasets with latent contexts

arXiv: https://arxiv.org/abs/2306.12896 · HTML: https://mlsanity.com/paper/2306.12896/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2023.

## Built on by (most cited first, 16 of 16)

- [SpaceTime: Causal Discovery from Non-Stationary Time Series](https://mlsanity.com/paper/2501.10235/) (2025)
- [Emergence of psychopathological computations in large language models](https://mlsanity.com/paper/2504.08016/) (2025)
- [CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series](https://mlsanity.com/paper/2410.02844/) (2024)
- [Causal Temporal Regime Structure Learning](https://mlsanity.com/paper/2311.01412/) (2023)
- [Identifying Nonstationary Causal Structures with High-Order Markov Switching Models](https://mlsanity.com/paper/2406.17698/) (2024)
- [Unified Causality Analysis Based on the Degrees of Freedom](https://mlsanity.com/paper/2410.19469/) (2024)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)
- [Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families](https://mlsanity.com/paper/2606.02231/) (2026)
- [Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series](https://mlsanity.com/paper/2507.09439/) (2025)
- [Sample Complexity of Causal Identification with Temporal Heterogeneity](https://mlsanity.com/paper/2602.06899/) (2026)
- [Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure](https://mlsanity.com/paper/2603.25473/) (2026)
- [Toward Artificial Intelligence Enabled Earth System Coupling](https://mlsanity.com/paper/2604.03289/) (2026)
- [From Causal Graphs to Markovian States: Multi-Order State Exposure for Deep RL](https://mlsanity.com/paper/2605.07057/) (2026)
- [AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery](https://mlsanity.com/paper/2608.00198/) (2026)
- [Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets](https://mlsanity.com/paper/2609.05150/) (2026)
- [Concept Drift from a Causal Perspective](https://mlsanity.com/paper/2609.25340/) (2026)

## Abstract (arXiv)

Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time series of river runoff from different catchments. The local catchment systems then share certain causal parents, such as time-dependent large-scale weather over all catchments, but differ in other catchment-specific drivers, such as the altitude of the catchment. These drivers can be called temporal and spatial contexts, respectively, and are often partially unobserved. Pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables enables us to overcome such latent confounding of system variables. In this work, we present a non-parametric time series causal discovery method, J(oint)-PCMCI+, that efficiently learns such joint causal time series graphs when both observed and latent contexts are present, including time lags. We present asymptotic consistency results and numerical experiments demonstrating the utility and limitations of the method.