Causal effects in longitudinal studies: Definition and maximum likelihood estimation

DOI10.1016/j.csda.2006.06.013zbMath1157.62377OpenAlexW2022387007MaRDI QIDQ1010518

Romain Neugebauer, Mark J. Van der Laan

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.06.013

zbMATH Keywords

longitudinal datamaximum likelihoodnonparametriccausal inferencemarginal structural modelg-computation

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Related Items

Does Marriage Boost Men’s Wages?: Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data, G-computation estimation for causal inference with complex longitudinal data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonparametric causal effects based on marginal structural models
• Bayesian inference for causal effects: The role of randomization
• Association, causation, and marginal structural models.
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Why prefer double robust estimators in causal inference?
• Causal inference for complex longitudinal data: the continuous case.
• Unified methods for censored longitudinal data and causality
• Asymptotic Statistics
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect