6 papers
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…
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…
Decoding the mechanisms of the Hattrick football manager game using Bayesian network structure learning
Anthony C. Constantinou, Nicholas Higgins, Neville K. Kitson
Hattrick is a free web-based probabilistic football manager game with over 200,000 users competing for titles at national and international levels. Launched in Sweden in 1997 as pa…
Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms
Neville K. Kitson, Anthony C. Constantinou
Many Bayesian Network structure learning algorithms are unstable, with the learned graph sensitive to arbitrary dataset artifacts, such as the ordering of columns (i.e., variable o…
Investigating potential causes of Sepsis with Bayesian network structure learning
Bruno Petrungaro, Neville K. Kitson, Anthony C. Constantinou
Sepsis is a life-threatening and serious global health issue. This study combines knowledge with available hospital data to investigate the potential causes of Sepsis that can be a…
Using GPT-4 to guide causal machine learning
Anthony C. Constantinou, Neville K. Kitson, Alessio Zanga
Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been cr…