Gilthorpe, Mark S.

Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H. (2017) · International Journal of Epidemiology

Advanced Causal Inference TechniquesBayesian Modeling and Causal Inference

Cited by 2,295

Time to reality check the promises of machine learning-powered precision medicine.

Wilkinson, J., Arnold, K. F., Murray, E. J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., et al. (2020) · The Lancet Digital Health

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 233

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Law, G. R., Gilthorpe, M. S., Secher, A. L., Temple, R., Bilous, R., Mathiesen, E. R., Murphy, H. R., & Scott, E. M. (2017) · Diabetologia

Gestational Diabetes Research and ManagementDiabetes Management and Research

Cited by 77

Model Selection of the Effect of Binary Exposures over the Life Course

Smith, A. D. A. C., Heron, J., Mishra, G., Gilthorpe, M. S., Ben-Shlomo, Y., & Tilling, K. (2015) · Epidemiology

Statistical Methods and InferenceStatistical Methods in Epidemiology

Cited by 63