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Karla Diaz-Ordaz

Showing results (1-10 of 44) with videos related to

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Psychometrika|May 19, 2021
Robust Inference for Mediated Effects in Partially Linear ModelsOliver Hines, Stijn Vansteelandt, Karla Diaz-Ordaz
American Journal of Epidemiology|February 17, 2021
Invited Commentary: Treatment Drop-in-Making the Case for Causal PredictionMatthew Sperrin, Karla Diaz-Ordaz, Romin Pajouheshnia
Biometrics|December 24, 2025
Variable importance measures for heterogeneous treatment effectsOliver J Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
Statistical Methods in Medical Research|May 15, 2016
Missing continuous outcomes under covariate dependent missingness in cluster randomised trialsAnower Hossain, Karla Diaz-Ordaz, Jonathan W Bartlett
Biostatistics (Oxford, England)|May 19, 2026
IV-learner: learning conditional average treatment effects using instrumental variablesStijn Vansteelandt, Stephen O'Neill, Richard Grieve, et al.
Physiological Genomics|July 21, 2020
Causal graphs for the analysis of genetic cohort dataOliver Hines, Karla Diaz-Ordaz, Stijn Vansteelandt, et al.
BMC Medical Research Methodology|October 24, 2013
A systematic review of cluster randomised trials in residential facilities for older people suggests how to improve qualityKarla Diaz-Ordaz, Robert Froud, Bart Sheehan, et al.
Journal of Clinical Epidemiology|September 14, 2024
Implementation of a dynamic model updating pipeline provides a systematic process for maintaining performance of prediction modelsKamaryn T Tanner, Karla Diaz-Ordaz, Ruth H Keogh
Biometrics|July 31, 2025
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome dataMatthew Pryce, Karla Diaz-Ordaz, Ruth H Keogh, et al.
BMC Medical Research Methodology|February 22, 2025
Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case studyEleanor Van Vogt, Anthony C Gordon, Karla Diaz-Ordaz, et al.
Pageof 5

Showing results (1-10 of 44) with videos related to

Sort By:
Pageof 5
Psychometrika|May 19, 2021
Robust Inference for Mediated Effects in Partially Linear ModelsOliver Hines, Stijn Vansteelandt, Karla Diaz-Ordaz
American Journal of Epidemiology|February 17, 2021
Invited Commentary: Treatment Drop-in-Making the Case for Causal PredictionMatthew Sperrin, Karla Diaz-Ordaz, Romin Pajouheshnia
Biometrics|December 24, 2025
Variable importance measures for heterogeneous treatment effectsOliver J Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
Statistical Methods in Medical Research|May 15, 2016
Missing continuous outcomes under covariate dependent missingness in cluster randomised trialsAnower Hossain, Karla Diaz-Ordaz, Jonathan W Bartlett
Biostatistics (Oxford, England)|May 19, 2026
IV-learner: learning conditional average treatment effects using instrumental variablesStijn Vansteelandt, Stephen O'Neill, Richard Grieve, et al.
Physiological Genomics|July 21, 2020
Causal graphs for the analysis of genetic cohort dataOliver Hines, Karla Diaz-Ordaz, Stijn Vansteelandt, et al.
BMC Medical Research Methodology|October 24, 2013
A systematic review of cluster randomised trials in residential facilities for older people suggests how to improve qualityKarla Diaz-Ordaz, Robert Froud, Bart Sheehan, et al.
Journal of Clinical Epidemiology|September 14, 2024
Implementation of a dynamic model updating pipeline provides a systematic process for maintaining performance of prediction modelsKamaryn T Tanner, Karla Diaz-Ordaz, Ruth H Keogh
Biometrics|July 31, 2025
Causal machine learning for heterogeneous treatment effects in the presence of missing outcome dataMatthew Pryce, Karla Diaz-Ordaz, Ruth H Keogh, et al.
BMC Medical Research Methodology|February 22, 2025
Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case studyEleanor Van Vogt, Anthony C Gordon, Karla Diaz-Ordaz, et al.
Pageof 5