10 citations · 17 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
Causal discovery using dynamically requested knowledge
Neville K Kitson, Anthony C Constantinou
Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key chal…
Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu +5
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algor…
The Impact of Variable Ordering on Bayesian Network Structure Learning
Neville K Kitson, Anthony C Constantinou
Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can…
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Kiattikun Chobtham, Anthony C. Constantinou, Neville K. Kitson
In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to…
Effective and efficient structure learning with pruning and model averaging strategies
Anthony C. Constantinou, Yang Liu, Neville K. Kitson +2
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a gi…
A survey of Bayesian Network structure learning
Neville K. Kitson, Anthony C. Constantinou, Zhigao Guo +2
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology…
The impact of prior knowledge on causal structure learning
Anthony C. Constantinou, Zhigao Guo, Neville K. Kitson
Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions t…
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham +2
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case…
Learning Bayesian networks from demographic and health survey data
Neville Kenneth Kitson, Anthony C. Constantinou
Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available…