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)

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

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

Evaluating counterfactual explanations using Pearl's counterfactual method. - Bevan I. Smith (CoRR, 2023)

Causal Discovery and Counterfactual Explanations for Personalized Student Learning. - Bevan I. Smith (CoRR, 2023)