Research map / Probabilistic and scientific modeling / Causal discovery
Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
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Proposes Meta-D²AG, an online meta-learning algorithm for discovering dynamic DAG structures from interventional time series data.
Problem
Causal discovery of directed acyclic graphs for dynamic time series, where existing work mostly assumes observational, offline, or stationary settings.
Approach
Applies online meta-learning to learn dynamic DAG structures from potentially nonlinear, interventional dynamic data, moving beyond observational, offline, and stationary assumptions.
Closest papers by meaning
- Interventional Causal Discovery in a Mixture of DAGs (NeurIPS 2024 · similarity 0.88)
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