Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
Fuente:
arXiv
Guardado en:
| Autores principales: | Pryce, Matthew, Diaz-Ordaz, Karla, Keogh, Ruth H., Vansteelandt, Stijn |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
por: Pryce, Matthew, et al.
Publicado: (2026)
por: Pryce, Matthew, et al.
Publicado: (2026)
Variable importance measures for heterogeneous treatment effects
por: Hines, Oliver J., et al.
Publicado: (2022)
por: Hines, Oliver J., et al.
Publicado: (2022)
Parameterising the effect of a continuous treatment using average derivative effects
por: Hines, Oliver J., et al.
Publicado: (2021)
por: Hines, Oliver J., et al.
Publicado: (2021)
Optimally weighted average derivative effects
por: Hines, Oliver, et al.
Publicado: (2023)
por: Hines, Oliver, et al.
Publicado: (2023)
Causal machine learning for predicting treatment outcomes
por: Feuerriegel, Stefan, et al.
Publicado: (2024)
por: Feuerriegel, Stefan, et al.
Publicado: (2024)
Evaluating the impact of longitudinal treatment strategies in the presence of informative monitoring and time-dependent confounding
por: Pirondini, Leah, et al.
Publicado: (2026)
por: Pirondini, Leah, et al.
Publicado: (2026)
Robust Inference for Mediated Effects in Partially Linear Models
por: Hines, Oliver, et al.
Publicado: (2020)
por: Hines, Oliver, et al.
Publicado: (2020)
Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
por: Salvadé, Nicolas, et al.
Publicado: (2025)
por: Salvadé, Nicolas, et al.
Publicado: (2025)
Flexible variable selection in the presence of missing data
por: Williamson, B. D., et al.
Publicado: (2022)
por: Williamson, B. D., et al.
Publicado: (2022)
To impute or not to impute: How machine learning modelers treat missing data
por: Chen, Wanyi, et al.
Publicado: (2025)
por: Chen, Wanyi, et al.
Publicado: (2025)
Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
por: Sun, Yuming, et al.
Publicado: (2026)
por: Sun, Yuming, et al.
Publicado: (2026)
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
por: Karlsson, Rickard, et al.
Publicado: (2025)
por: Karlsson, Rickard, et al.
Publicado: (2025)
Handling missing values in clinical machine learning: Insights from an expert study
por: Stempfle, Lena, et al.
Publicado: (2024)
por: Stempfle, Lena, et al.
Publicado: (2024)
Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning
por: Aydin, Seyda Betul, et al.
Publicado: (2025)
por: Aydin, Seyda Betul, et al.
Publicado: (2025)
Causal machine learning for sustainable agroecosystems
por: Sitokonstantinou, Vasileios, et al.
Publicado: (2024)
por: Sitokonstantinou, Vasileios, et al.
Publicado: (2024)
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
por: Zhang, Xiaojian, et al.
Publicado: (2022)
por: Zhang, Xiaojian, et al.
Publicado: (2022)
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
por: Zhang, Handi, et al.
Publicado: (2024)
por: Zhang, Handi, et al.
Publicado: (2024)
Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
por: Colwell, Matthew, et al.
Publicado: (2024)
por: Colwell, Matthew, et al.
Publicado: (2024)
Domain constraints improve risk prediction when outcome data is missing
por: Balachandar, Sidhika, et al.
Publicado: (2023)
por: Balachandar, Sidhika, et al.
Publicado: (2023)
Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
por: Chen, Tong, et al.
Publicado: (2025)
por: Chen, Tong, et al.
Publicado: (2025)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
por: Kempen, Luuk H. E., et al.
Publicado: (2025)
por: Kempen, Luuk H. E., et al.
Publicado: (2025)
Symmetry breaking in geometric quantum machine learning in the presence of noise
por: Tüysüz, Cenk, et al.
Publicado: (2024)
por: Tüysüz, Cenk, et al.
Publicado: (2024)
Causal hybrid modeling with double machine learning
por: Cohrs, Kai-Hendrik, et al.
Publicado: (2024)
por: Cohrs, Kai-Hendrik, et al.
Publicado: (2024)
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
por: Ellul, Susan, et al.
Publicado: (2024)
por: Ellul, Susan, et al.
Publicado: (2024)
SEGAN: semi-supervised learning approach for missing data imputation
por: Pan, Xiaohua, et al.
Publicado: (2024)
por: Pan, Xiaohua, et al.
Publicado: (2024)
Debiasing Synthetic Data Generated by Deep Generative Models
por: Decruyenaere, Alexander, et al.
Publicado: (2024)
por: Decruyenaere, Alexander, et al.
Publicado: (2024)
The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data
por: Decruyenaere, Alexander, et al.
Publicado: (2023)
por: Decruyenaere, Alexander, et al.
Publicado: (2023)
The super learner for time-to-event outcomes: A tutorial
por: Keogh, Ruth H., et al.
Publicado: (2025)
por: Keogh, Ruth H., et al.
Publicado: (2025)
Causality in the human niche: lessons for machine learning
por: Lange, Richard D., et al.
Publicado: (2025)
por: Lange, Richard D., et al.
Publicado: (2025)
Website visits can predict angler presence using machine learning
por: Schmid, Julia S., et al.
Publicado: (2024)
por: Schmid, Julia S., et al.
Publicado: (2024)
REX: Causal discovery based on machine learning and explainability techniques
por: Renero, Jesus, et al.
Publicado: (2025)
por: Renero, Jesus, et al.
Publicado: (2025)
Explainable AI for survival analysis: a median-SHAP approach
por: Ter-Minassian, Lucile, et al.
Publicado: (2024)
por: Ter-Minassian, Lucile, et al.
Publicado: (2024)
Machine-learning-based particle identification with missing data
por: Kasak, Miłosz, et al.
Publicado: (2023)
por: Kasak, Miłosz, et al.
Publicado: (2023)
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
por: Kallus, Nathan, et al.
Publicado: (2020)
por: Kallus, Nathan, et al.
Publicado: (2020)
The influence of missing data mechanisms and simple missing data handling techniques on fairness
por: Bhatti, Aeysha, et al.
Publicado: (2025)
por: Bhatti, Aeysha, et al.
Publicado: (2025)
Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
por: Frauen, Dennis, et al.
Publicado: (2024)
por: Frauen, Dennis, et al.
Publicado: (2024)
Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
por: Wang, Yuxin, et al.
Publicado: (2025)
por: Wang, Yuxin, et al.
Publicado: (2025)
Personalized federated prototype learning in mixed heterogeneous data scenarios
por: Zeng, Jiahao, et al.
Publicado: (2025)
por: Zeng, Jiahao, et al.
Publicado: (2025)
Developing robust methods to handle missing data in real-world applications effectively
por: Zhou, Youran, et al.
Publicado: (2025)
por: Zhou, Youran, et al.
Publicado: (2025)
On the consistency of supervised learning with missing values
por: Josse, Julie, et al.
Publicado: (2019)
por: Josse, Julie, et al.
Publicado: (2019)
Ejemplares similares
-
Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
por: Pryce, Matthew, et al.
Publicado: (2026) -
Variable importance measures for heterogeneous treatment effects
por: Hines, Oliver J., et al.
Publicado: (2022) -
Parameterising the effect of a continuous treatment using average derivative effects
por: Hines, Oliver J., et al.
Publicado: (2021) -
Optimally weighted average derivative effects
por: Hines, Oliver, et al.
Publicado: (2023) -
Causal machine learning for predicting treatment outcomes
por: Feuerriegel, Stefan, et al.
Publicado: (2024)