# Neural Additive Vector Autoregression Models for Causal Discovery in Time Series

arXiv: https://arxiv.org/abs/2010.09429 · HTML: https://mlsanity.com/paper/2010.09429/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [A Survey on Causal Discovery Methods for I.I.D. and Time Series Data](https://mlsanity.com/paper/2303.15027/) (2023)
- [Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise](https://mlsanity.com/paper/2210.14706/) (2022)
- [Neural Structure Learning with Stochastic Differential Equations](https://mlsanity.com/paper/2311.03309/) (2023)
- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [Jacobian Regularizer-based Neural Granger Causality](https://mlsanity.com/paper/2405.08779/) (2024)
- [Deep Recurrent Modelling of Granger Causality with Latent Confounding](https://mlsanity.com/paper/2202.11286/) (2022)
- [CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data](https://mlsanity.com/paper/2302.03246/) (2023)
- [Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour](https://mlsanity.com/paper/2302.00064/) (2023)
- [Double Machine Learning Based Structure Identification from Temporal Data](https://mlsanity.com/paper/2311.06012/) (2023)
- [DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series](https://mlsanity.com/paper/2511.02137/) (2025)
- [Sortability of Time Series Data](https://mlsanity.com/paper/2407.13313/) (2024)
- [Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises](https://mlsanity.com/paper/2506.17065/) (2025)
- [Causal Temporal Regime Structure Learning](https://mlsanity.com/paper/2311.01412/) (2023)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series](https://mlsanity.com/paper/2308.08148/) (2023)
- [Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs](https://mlsanity.com/paper/2402.03614/) (2024)
- [Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series](https://mlsanity.com/paper/2505.20697/) (2025)
- [When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic](https://mlsanity.com/paper/2606.08390/) (2026)
- [GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series](https://mlsanity.com/paper/2606.23880/) (2026)
- [Efficient Interpretable Nonlinear Modeling for Multiple Time Series](https://mlsanity.com/paper/2309.17154/) (2023)
- [LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models](https://mlsanity.com/paper/2410.20053/) (2024)
- [SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input](https://mlsanity.com/paper/2501.03130/) (2025)
- [A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes](https://mlsanity.com/paper/2507.11178/) (2025)
- [PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery](https://mlsanity.com/paper/2605.01669/) (2026)
- [Function-Valued Causal Influence in Nonlinear Time Series](https://mlsanity.com/paper/2605.26408/) (2026)
- [Trajectory-Aware Node Contributions and the Limits of Static Controllability](https://mlsanity.com/paper/2606.03067/) (2026)
- [LUCID: Learning Under Confounding for Inference and Discovery in Time Series](https://mlsanity.com/paper/2609.31315/) (2026)
- [Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization](https://mlsanity.com/paper/2609.34115/) (2026)

## Abstract (arXiv)

Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time series data sets are usually available. Current methods that learn causal structure from time series often assume linear relationships. Hence, they may fail in realistic settings that contain nonlinear relations between the variables. We propose Neural Additive Vector Autoregression (NAVAR) models, a neural approach to causal structure learning that can discover nonlinear relationships. We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series. The method achieves state-of-the-art results on various benchmark data sets for causal discovery, while providing clear interpretations of the mapped causal relations.