# Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data

arXiv: https://arxiv.org/abs/2406.10419 · HTML: https://mlsanity.com/paper/2406.10419/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Differentiable Causal Discovery from Interventional Data](https://mlsanity.com/paper/2007.01754/) (2020)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes](https://mlsanity.com/paper/1903.01672/) (2019)
- [Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions](https://mlsanity.com/paper/1802.06310/) (2018)
- [Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality](https://mlsanity.com/paper/1911.09879/) (2019)
- [Interpretable Models for Granger Causality Using Self-explaining Neural Networks](https://mlsanity.com/paper/2101.07600/) (2021)
- [Causal Recurrent Variational Autoencoder for Medical Time Series Generation](https://mlsanity.com/paper/2301.06574/) (2023)
- [CUTS+: High-dimensional Causal Discovery from Irregular Time-series](https://mlsanity.com/paper/2305.05890/) (2023)
- [Inductive Granger Causal Modeling for Multivariate Time Series](https://mlsanity.com/paper/2102.05298/) (2021)
- [Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes](https://mlsanity.com/paper/2209.04480/) (2022)
- [Towards Invariant Time Series Forecasting in Smart Cities](https://mlsanity.com/paper/2405.05430/) (2024)
- [A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations](https://mlsanity.com/paper/1511.00370/) (2015)

## Built on by (most cited first, 4 of 4)

- [Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences](https://mlsanity.com/paper/2409.13857/) (2024)
- [Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity](https://mlsanity.com/paper/2510.19138/) (2025)
- [DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data](https://mlsanity.com/paper/2602.01433/) (2026)
- [CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts](https://mlsanity.com/paper/2606.13024/) (2026)

## Abstract (arXiv)

Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network prediction models. To alleviate challenges in better deciphering causal structures unambiguously from time series, the use of interventional data has become a practical approach. However, existing methods have yet to be explored in the context of imperfect interventions with unknown targets, which are more common and often more beneficial in a wide range of real-world applications. Additionally, the identifiability issues of Granger causality with unknown interventional targets in complex network models remain unsolved. Our work presents a theoretically-grounded method that infers Granger causal structure and identifies unknown targets by leveraging heterogeneous interventional time series data. We further illustrate that learning Granger causal structure and recovering interventional targets can mutually promote each other. Comparative experiments demonstrate that our method outperforms several robust baseline methods in learning Granger causal structure from interventional time series data.