7 citations · 10 across the 3 of their papers we have counts for
6 papers
Bayesian network structure learning with causal effects in the presence of latent variables
Kiattikun Chobtham, Anthony C. Constantinou
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal…
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 that enable full propagation of evidence
Anthony Constantinou
This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can…
Simpson's Paradox and the implications for medical trials
Norman Fenton, Martin Neil, Anthony Constantinou
This paper describes Simpson's paradox, and explains its serious implications for randomised control trials. In particular, we show that for any number of variables we can simulate…
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…
Evaluating structure learning algorithms with a balanced scoring function
Anthony C. Constantinou
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by asses…