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cs.AI2026
LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation
Neville K. Kitson, Anthony Constantinou
Bayesian network structure learning (BNSL) from observational data struggles with orientation identifiability, while large language models (LLMs) offer broad but often unreliable c…
cs.LG2026
Time series causal discovery with variable lags
Bruno Petrungaro, Anthony C. Constantinou
Causal Bayesian Networks (CBNs) are a powerful tool for reasoning under uncertainty about complex real-world problems. Such problems evolve over time, responding to external shocks…
cs.LG2026
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
Bruno Petrungaro, Anthony C. Constantinou
Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no…