Generalizing treatment effects with incomplete covariates: identifying assumptions and multiple imputation algorithms
From MaRDI portal
Publication:6563657
DOI10.1002/bimj.202100294zbMATH Open1541.62312MaRDI QIDQ6563657
Publication date: 27 June 2024
Published in: Biometrical Journal (Search for Journal in Brave)
data integrationexternal validitymultiple imputationmissing valuesrandom forestcausal effect transportabilitymissing incorporated in attributes
Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05)
Cites Work
- Unnamed Item
- Unnamed Item
- Generalized random forests
- Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
- A causal bootstrap
- Doubly robust treatment effect estimation with missing attributes
- Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework
- Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness
- Estimating treatment effects with partially observed covariates using outcome regression with missing indicators
- Clarifying missing at random and related definitions, and implications when coupled with exchangeability: Table 1.
- Inference and missing data
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
- Balancing Covariates via Propensity Score Weighting
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Double/debiased machine learning for treatment and structural parameters
- Causal inference with confounders missing not at random
- Flexible Imputation of Missing Data, Second Edition
- Random forests
- High-Dimensional Principal Component Analysis with Heterogeneous Missingness
- Improving Trial Generalizability Using Observational Studies
- Using simulation studies to evaluate statistical methods
Related Items (1)
This page was built for publication: Generalizing treatment effects with incomplete covariates: identifying assumptions and multiple imputation algorithms