Why prefer double robust estimators in causal inference?

DOI10.1016/j.jspi.2004.06.060zbMath1058.62106OpenAlexW1991042960MaRDI QIDQ1765677

Romain Neugebauer, Mark J. Van der Laan

Publication date: 23 February 2005

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2004.06.060

zbMATH Keywords

Censored dataRobustnessCausal inferenceDouble robust estimatorG-computation estimatorInverse probability of treatment weighted estimatorMarginal structural model

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Applications of statistics (62P99)

Related Items

Nonparametric Tests of the Causal Null With Nondiscrete Exposures, Data-adaptive estimation of the treatment-specific mean, Nonparametric causal effects based on marginal structural models, Causal inference in case of near‐violation of positivity: comparison of methods, Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models, Doubly Robust Estimation in Missing Data and Causal Inference Models, Causal effects in longitudinal studies: Definition and maximum likelihood estimation, Augmented weighted estimators dealing with practical positivity violation to causal inferences in a random coefficient model, Performance of the marginal structural models under various scenarios of incomplete marker's values: A simulation study, Combining the complete-data and nonresponse models for drawing imputations under MAR, Double robust estimation in longitudinal marginal structural models

Cites Work

• Association, causation, and marginal structural models.
• Unified methods for censored longitudinal data and causality
• Inference and missing data
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• Unnamed Item
• Unnamed Item