Links to personal research websites and Google scholar publications:

Dr Anthony C. Constantinou [web][Google scholar]

Dr Neville Kenneth Kitson[Google scholar]

Dr Rendani Mbuvha [web][Google scholar]

Dr Zhigao Guo[Google scholar]

Mr Yang Liu[Google scholar]

Mr Kiattikun Chobtham[Google scholar]

Mr Austin Plunkett[Google scholar]

Featured publications:


  • Kitson, N. K., and Constantinou, A. (2023). Causal discovery using dynamically requested knowledge. [arXiv:2310.11154] [cs.AI]
  • Constantinou, A. C., Kitson, N. K., Liu, Y., Chobtham, K., Hashemzadeh, A., Nanavati, P. A., Mbuvha, R., and Petrungaro, B. (2023). Open problems in causal structure learning: A case study of COVID-19 in the UK. Expert Systems with Applications, vol. 234, Article 121069. [Open-Access DOI]
  • Liu, Y., and Constantinou, A. (2023). Improving the imputation of missing data with Markov Blanket discovery. In Proceedings of the 11th International Conference on Learning Representations (ICLR-2023), Kigali, Rwanda. [Proceedings download]
  • Kitson, N. K., Constantinou, A., Guo, Z., Liu, Y., and Chobtham, K. (2023). A survey of Bayesian network structure learning. Artificial Intelligence Review, vol. 56, pp. 8721–8814. [Open-Access DOI]
  • Constantinou, A., Guo, Z., and Kitson, N. K. (2023). The impact of prior knowledge on causal structure learning. Knowledge and Information Systems, vol. 65, pp. 3385–3434. [Open-Access DOI]


  • Kitson, N. K., and Constantinou, A. C. (2022). The Impact of Variable Ordering on Bayesian Network Structure Learning. arXiv:2206.08952
  • Chobtham, K., and Constantinou, A. C. (2022). Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound. In Proceedings of the 11th International Conference on Probabilistic Graphical Models (PGM-2022), Almeria, Spain, Oct 2022 [PMLR Proceedings download]
  • Liu, Y., and Constantinou, A. (2022). Greedy structure learning from data that contain systematic missing values. Machine Learning, vol. 111, pp. 3867–3896. [Open-Access DOI]
  • Constantinou, A. C., Liu, Y., Kitson, N. K., Chobtham, K., and Guo, Z. (2022). Effective and efficient structure learning with pruning and model averaging strategies. International Journal of Approximate Reasoning, vol. 151, pp. 292–321. [Open-Access DOI]
  • Liu, Y., Constantinou, A. C., and Zhigao, G. (2022). Improving Bayesian network structure learning in the presence of measurement error. Journal of Machine Learning Research, Vol. 23, Iss. 324, pp. 1–28. [Open-Access DOI]
  • Chobtham, K., Constantinou, A. C., and Kitson, N. K. (2022). Hybrid Bayesian network discovery with latent variables by scoring multiple interventions. Data Mining and Knowledge Discovery, Vol. 37, pp.476–520. [Open-Access DOI]
  • Guo, Z. and Constantinou, A. C. (2022). Parallel Sampling for efficient high-dimensional Bayesian network structure learning. arXiv:2202.09691 [cs.LG]
  • Constantinou, A. (2022). Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks. Journal of Sports Analytics, vol. 8, pp. 171-193. [Open-Access DOI]


  • Constantinou, A. C., Liu, Y., Chobtham, K., Guo, Z., and Kitson, N. K. (2021). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. International Journal of Approximate Reasoning, Vol. 131, pp. 151-188. [Open-access DOI]
  • Kitson, N. K., & Constantinou, A. (2021). Learning Bayesian networks from demographic and health survey data. Journal of Biomedical Informatics, Vol. 113, Article 103588. [Open-Access DOI].
  • Constantinou, A. C. (2021). The importance of temporal information in Bayesian network structure learning. Expert Systems with Applications, Vol. 164, Article 113814. [Open-access DOI]


  • Guo, Z. and Constantinou, A. C. (2020). Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets. Entropy, Vol. 22, Iss. 10, Article 1142. [Open-access DOI]
  • Chobtham, K. and Constantinou, A. C. (2020). Bayesian network structure learning with causal effects in the presence of latent variables. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM-2020), Aalborg, Denmark. [PMLR Proceedings download]
  • Constantinou, A. C. (2020). Learning Bayesian Networks that enable full propagation of evidence. IEEE Access, Vol. 8, pp. 124845-124856 [Open-Access DOI].
  • Fenton, N., Neil, M., & Constantinou, A. (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). Artificial Intelligence, Vol. 284, Article 103286. [DOI]


  • Constantinou, A. C. (2019). Evaluating structure learning algorithms with a balanced scoring function. arXiv:1905.12666[cs.LG]
  • Constantinou, A., & Fenton, N. (2018). Things to know about Bayesian Networks. Significance, Vol. 15, Iss. 2, pp. 19–23. [Open Access DOI]