10 citations · 56 across the 19 of their papers we have counts for
21 papers
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…
Tuning structure learning algorithms with out-of-sample and resampling strategies
Kiattikun Chobtham, Anthony C. Constantinou
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter…
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…
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
Kiattikun Chobtham, Anthony C. Constantinou
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we f…
Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning
Zhigao Guo, Anthony C. Constantinou
Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number…