Cluster-level cross-fitting is necessary for valid design-based inference of survey-weighted TMLE with flexible machine-learning nuisances under stratified multistage sampling.
Scandinavian Journal of Statistics , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Tutorial reviewing and comparing methods to correct measurement error in outcomes and multiple covariates, with a running example, data, and code for reproduction.
citing papers explorer
-
Cross-Fitted Survey-Weighted TMLE with Design-Based Variance for Causal Machine Learning
Cluster-level cross-fitting is necessary for valid design-based inference of survey-weighted TMLE with flexible machine-learning nuisances under stratified multistage sampling.
-
Methods to address measurement error in both Outcome and Covariates
Tutorial reviewing and comparing methods to correct measurement error in outcomes and multiple covariates, with a running example, data, and code for reproduction.