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

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

27 May 2025

Zachary C. Brown

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

13 / 13 papers shown

Title
Adjustment Identification Distance: A gadjid for Causal Structure Learning Leonard Henckel Theo Würtzen Sebastian Weichwald CML 55 9 0 13 Feb 2024
Multiscale Causal Structure Learning Gabriele DÁcunto P. Lorenzo Sergio Barbarossa 73 6 0 16 Jul 2022
Causal Discovery from Conditionally Stationary Time Series Carles Balsells-Rodas Ruibo Tu Tanmayee Narendra Yingzhen Li Gabriele Schweikert Hedvig Kjellström Yingzhen Li AI4TS BDL CML 117 6 0 12 Oct 2021
Granger Causality: A Review and Recent Advances Ali Shojaie E. Fox CML AI4TS 63 269 0 05 May 2021
Neural Additive Vector Autoregression Models for Causal Discovery in Time Series Bart Bussmann Jannes Nys Steven Latré CML BDL 25 25 0 19 Oct 2020
High-recall causal discovery for autocorrelated time series with latent confounders Andreas Gerhardus J. Runge CML AI4TS 48 102 0 03 Jul 2020
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders Austin Talbot David B. Dunson K. Dzirasa David Carlson 13 4 0 10 Apr 2020
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets Jakob Runge 57 193 0 07 Mar 2020
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values S. Weichwald M. E. Jakobsen Phillip B. Mogensen Lasse Petersen Nikolaj Thams Gherardo Varando CML AI4TS 73 27 0 21 Feb 2020
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Erdun Gao Kun Zhang Biwei Huang Clark Glymour CML AI4TS 53 64 0 26 May 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 69 486 0 22 Apr 2019
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems Oliver J. Maclaren R. Nicholson 49 35 0 04 Apr 2019
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems E. Fox Erik B. Sudderth Michael I. Jordan A. Willsky 86 244 0 19 Mar 2010