On the use of cross-fitting in causal machine learning with correlated units
Fuente:
arXiv
Saved in:
| Main Authors: | Balkus, Salvador V., Laith, Hasan, Hejazi, Nima S. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The causal effects of modified treatment policies under network interference
by: Balkus, Salvador V., et al.
Published: (2024)
by: Balkus, Salvador V., et al.
Published: (2024)
A Riesz Representer Perspective on Targeted Learning
by: Balkus, Salvador V., et al.
Published: (2026)
by: Balkus, Salvador V., et al.
Published: (2026)
Linear models for causal inference under network interference
by: Tong, Eric, et al.
Published: (2026)
by: Tong, Eric, et al.
Published: (2026)
Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
by: Jiang, Cong, et al.
Published: (2026)
by: Jiang, Cong, et al.
Published: (2026)
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
by: Ellul, Susan, et al.
Published: (2024)
by: Ellul, Susan, et al.
Published: (2024)
Causal survival analysis under competing risks using longitudinal modified treatment policies
by: Díaz, Iván, et al.
Published: (2022)
by: Díaz, Iván, et al.
Published: (2022)
Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
by: Lu, Xin, et al.
Published: (2025)
by: Lu, Xin, et al.
Published: (2025)
Statistical learning for constrained functional parameters in infinite-dimensional models
by: Nabi, Razieh, et al.
Published: (2024)
by: Nabi, Razieh, et al.
Published: (2024)
Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions
by: Boileau, Philippe, et al.
Published: (2021)
by: Boileau, Philippe, et al.
Published: (2021)
Design-based edge-level causal inference with machine learning assisted covariate adjustment
by: Yu, Haoyang, et al.
Published: (2026)
by: Yu, Haoyang, et al.
Published: (2026)
A nonparametric framework for treatment effect modifier discovery in high dimensions
by: Boileau, Philippe, et al.
Published: (2023)
by: Boileau, Philippe, et al.
Published: (2023)
Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
by: Barnatchez, Keith, et al.
Published: (2025)
by: Barnatchez, Keith, et al.
Published: (2025)
A scalable Bayesian double machine learning framework for high dimensional causal estimation, with application to racial disproportionality assessment
by: Luo, Yu, et al.
Published: (2025)
by: Luo, Yu, et al.
Published: (2025)
Path-specific causal decomposition analysis with multiple correlated mediator variables
by: Smith, Melissa J., et al.
Published: (2023)
by: Smith, Melissa J., et al.
Published: (2023)
Active Multiple-Prediction-Powered Inference
by: Brawand, Nicholas, et al.
Published: (2026)
by: Brawand, Nicholas, et al.
Published: (2026)
Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning
by: Mucesh, Sunil, et al.
Published: (2024)
by: Mucesh, Sunil, et al.
Published: (2024)
Assessing variable importance in survival analysis using machine learning
by: Wolock, Charles J., et al.
Published: (2023)
by: Wolock, Charles J., et al.
Published: (2023)
Efficient estimation of longitudinal treatment effects using difference-in-differences and machine learning
by: Illenberger, Nicholas, et al.
Published: (2024)
by: Illenberger, Nicholas, et al.
Published: (2024)
Extension of Dynamic Network Biomarker using the propensity score method: Simulation of causal effects on variance and correlation coefficient
by: Shinoda, Satoru, et al.
Published: (2025)
by: Shinoda, Satoru, et al.
Published: (2025)
Deep learning based doubly robust test for Granger causality
by: Hui, Yongchang, et al.
Published: (2025)
by: Hui, Yongchang, et al.
Published: (2025)
Diagnostic tools for a multivariate negative binomial model for fitting correlated data with overdispersion
by: Fabio, Lizandra Castilho, et al.
Published: (2021)
by: Fabio, Lizandra Castilho, et al.
Published: (2021)
Average partial effect estimation using double machine learning
by: Klyne, Harvey, et al.
Published: (2023)
by: Klyne, Harvey, et al.
Published: (2023)
Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables
by: Khan, Mariyam, et al.
Published: (2024)
by: Khan, Mariyam, et al.
Published: (2024)
Quantification and cross-fitting inference of asymmetric relations under generative exposure mapping models
by: Purkayastha, Soumik, et al.
Published: (2023)
by: Purkayastha, Soumik, et al.
Published: (2023)
Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
by: Chen, Tong, et al.
Published: (2025)
by: Chen, Tong, et al.
Published: (2025)
Identification and estimation of causal peer effects using instrumental variables
by: Luo, Shanshan, et al.
Published: (2025)
by: Luo, Shanshan, et al.
Published: (2025)
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
by: van der Laan, Lars, et al.
Published: (2024)
by: van der Laan, Lars, et al.
Published: (2024)
Post-selection inference for causal effects after causal discovery
by: Chang, Ting-Hsuan, et al.
Published: (2024)
by: Chang, Ting-Hsuan, et al.
Published: (2024)
General Bayesian inference for causal effects using covariate balancing procedure
by: Orihara, Shunichiro, et al.
Published: (2024)
by: Orihara, Shunichiro, et al.
Published: (2024)
Modifying causal models to distinguish between transient and lasting causal effects
by: Steele, Russell, et al.
Published: (2026)
by: Steele, Russell, et al.
Published: (2026)
Debiased machine learning for combining probability and non-probability survey data
by: Seaman, Shaun
Published: (2025)
by: Seaman, Shaun
Published: (2025)
Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
by: Boileau, Philippe, et al.
Published: (2026)
by: Boileau, Philippe, et al.
Published: (2026)
A powerful goodness-of-fit test using the probability integral transform of order statistics
by: Covington, Christian T., et al.
Published: (2025)
by: Covington, Christian T., et al.
Published: (2025)
Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data
by: Deng, Fei, et al.
Published: (2025)
by: Deng, Fei, et al.
Published: (2025)
Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)
by: Riley, Richard D, et al.
Published: (2022)
Causal hybrid modeling with double machine learning
by: Cohrs, Kai-Hendrik, et al.
Published: (2024)
by: Cohrs, Kai-Hendrik, et al.
Published: (2024)
Accounting for multiplicity in machine learning benchmark performance
by: Møllersen, Kajsa, et al.
Published: (2023)
by: Møllersen, Kajsa, et al.
Published: (2023)
Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials
by: Li, Changjun, et al.
Published: (2026)
by: Li, Changjun, et al.
Published: (2026)
Spatiotemporal double machine learning to estimate the impact of Cambodian land concessions on deforestation
by: Arifin, Anika, et al.
Published: (2026)
by: Arifin, Anika, et al.
Published: (2026)
Small area prediction of counts under machine learning-type mixed models
by: Frink, Nicolas, et al.
Published: (2024)
by: Frink, Nicolas, et al.
Published: (2024)
Similar Items
-
The causal effects of modified treatment policies under network interference
by: Balkus, Salvador V., et al.
Published: (2024) -
A Riesz Representer Perspective on Targeted Learning
by: Balkus, Salvador V., et al.
Published: (2026) -
Linear models for causal inference under network interference
by: Tong, Eric, et al.
Published: (2026) -
Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
by: Jiang, Cong, et al.
Published: (2026) -
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
by: Ellul, Susan, et al.
Published: (2024)