Random Forest Missing Data Imputation Methods: Implications for Predicting At-Risk Students. - Bevan I. Smith; Charles Chimedza; Jacoba H. Bührmann (ISDA, 2019)

Global and Individual Treatment Effects Using Machine Learning Methods. - Bevan I. Smith; Charles Chimedza; Jacoba H. Bührmann (Int. J. Artif. Intell. Educ., 2020)

Deep Learning for SARS COV-2 Genome Sequences. - Albert Whata; Charles Chimedza (IEEE Access, 2021)

Predicting treatment effects from observational studies using machine learning methods: A simulation study. - Bevan I. Smith; Charles Chimedza (CoRR, 2021)

A Machine Learning Evaluation of the Effects of South Africa's COVID-19 Lockdown Measures on Population Mobility. - Albert Whata; Charles Chimedza (Mach. Learn. Knowl. Extr., 2021)

Evaluating Uses of Deep Learning Methods for Causal Inference. - Albert Whata; Charles Chimedza (IEEE Access, 2022)

Individualized help for at-risk students using model-agnostic and counterfactual explanations. - Bevan I. Smith; Charles Chimedza; Jacoba H. Bührmann (Educ. Inf. Technol., 2022)

Comparing Discrimination and Calibration Performance of Two Flexible Link Functions in Discrete Survival Models. - Charles Chimedza; Caston Sigauke; Alphonce Bere; Susan Maposa (Int. J. Data Anal. Tech. Strateg., 2025)