# From Causal Discovery to Dynamic Causal Inference in Neural Time Series

arXiv: https://arxiv.org/abs/2603.20980 · HTML: https://mlsanity.com/paper/2603.20980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 92 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Causal Modeling of Dynamical Systems](https://mlsanity.com/paper/1803.08784/) (2018)
- [Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery](https://mlsanity.com/paper/2404.10356/) (2024)
- [What can we Learn from Predictive Modeling?](https://mlsanity.com/paper/1612.05844/) (2016)
- [Expressive architectures enhance interpretability of dynamics-based neural population models](https://mlsanity.com/paper/2212.03771/) (2022)
- [Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning](https://mlsanity.com/paper/2409.03632/) (2024)
- [Counterfactual-based Root Cause Analysis for Dynamical Systems](https://mlsanity.com/paper/2406.08106/) (2024)
- [Ridge Regularized Estimation of VAR Models for Inference](https://mlsanity.com/paper/2105.00860/) (2021)

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

- [Trajectory-Aware Reliability Modeling of Democratic Systems](https://mlsanity.com/paper/2604.20127/) (2026)
- [Function-Valued Causal Influence in Nonlinear Time Series](https://mlsanity.com/paper/2605.26408/) (2026)

## Abstract (arXiv)

Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable. This limits the applicability of dynamic causal inference in many scientific settings. We propose Dynamic Causal Network Autoregression (DCNAR), a two-stage neural causal modeling framework that integrates data-driven causal discovery with time-varying causal inference. In the first stage, a neural autoregressive causal discovery model learns a sparse directed causal network from multivariate time series. In the second stage, this learned structure is used as a structural prior for a time-varying neural network autoregression, enabling dynamic estimation of causal influence without requiring pre-specified network structure. We evaluate the scientific validity of DCNAR using behavioral diagnostics that assess causal necessity, temporal stability, and sensitivity to structural change, rather than predictive accuracy alone. Experiments on multi-country panel time-series data demonstrate that learned causal networks yield more stable and behaviorally meaningful dynamic causal inferences than coefficient-based or structure-free alternatives, even when forecasting performance is comparable. These results position DCNAR as a general framework for using AI as a scientific instrument for dynamic causal reasoning under structural uncertainty.