mlsanityData to 14 Oct 2026

GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network

arXiv 2302.10804 ↗First posted 28 Jan 2023cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system. It provides critical tools for forecasting, simulations and interventions in science and business analytics. In this paper, we proposed a graph neural network approach with score-based method aiming at learning a sparse DAG that captures the causal dependencies in a discretized time temporal graph. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference. In addition, from the experiments, the structural causal model can be more accurate than a linear SCM discovered by the methods such as Notears.

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References · 21

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9 more
  1. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
  2. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
  3. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 100
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  5. 2021Relating Graph Neural Networks to Structural Causal Modelscited by 41
  6. 2022Predicting the impact of treatments over time with uncertainty aware neural differential equationscited by 23
  7. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  8. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 12
  9. 2021Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundationscited by 3

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