mlsanityData to 14 Oct 2026

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series

arXiv 2505.20697 ↗First posted 27 May 2025cs.LG cs.AI stat.APcited by 1

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Abstract (arXiv)

The field of hypothesis generation promises to reduce costs in neuroscience by narrowing the range of interventional studies needed to study various phenomena. Existing machine learning methods can generate scientific hypotheses from complex datasets, but many approaches assume causal relationships are static over time, limiting their applicability to systems with dynamic, state-dependent behavior, such as the brain. While some techniques attempt dynamic causal discovery through factor models, they often restrict relationships to linear patterns or impose other simplifying assumptions. We propose a novel method that models dynamic graphs as a conditionally weighted superposition of static graphs, where each static graph can capture nonlinear relationships. This approach enables the detection of complex, time-varying interactions between variables beyond linear limitations. Our method improves f1-scores of predicted dynamic causal patterns by roughly 22-28% on average over baselines in some of our experiments, with some improvements reaching well over 60%. A case study on real brain data demonstrates our method's ability to uncover relationships linked to specific behavioral states, offering valuable insights into neural dynamics.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

13 of 46 references.

References · 13

  1. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  2. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
  3. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  4. 2021Granger Causality: A Review and Recent Advancescited by 63
  5. 2019Causal Discovery and Forecasting in Nonstationary Environments with State-Space Modelscited by 45
  6. 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
  7. 2020Neural Additive Vector Autoregression Models for Causal Discovery in Time Seriescited by 28
  8. 2020Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuescited by 18
  9. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  10. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 12
  11. 2019What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problemscited by 11
  12. 2022Multiscale Causal Structure Learningcited by 7
1 more
  1. 2020Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoderscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CauScale: Neural Causal Discovery at Scalecited by 6

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Close in wording, no citation either way

  1. 2026From Causal Discovery to Dynamic Causal Inference in Neural Time Series
  2. 2025CausalMamba: Scalable Conditional State Space Models for Neural Causal Inference
  3. 2025Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
  4. 2023Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach
  5. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
  6. 2026CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
  7. 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
  8. 2026TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery