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Open problems in causal structure learning: A case study of COVID-19 in the UK
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships.
Learning Bayesian Networks that enable full propagation of evidence
This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent.
Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks
Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature.
Simpson's Paradox and the implications for medical trials
This paper describes Simpson's paradox, and explains its serious implications for randomised control trials.
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