Study Design in Causal Models

DOI10.1111/SJOS.12110zbMath1378.62037arXiv1211.2958OpenAlexW2951813053WikidataQ55966967 ScholiaQ55966967MaRDI QIDQ5251481

Juha Karvanen

Publication date: 20 May 2015

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.2958

zbMATH Keywords

designcausalityepidemiologymissing datagraphical modelstructural equation model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Design of statistical experiments (62K99)

Related Items (2)

Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys ⋮ Bayesian models for data missing not at random in health examination surveys

Cites Work

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• Identification of Causal Effects Using Instrumental Variables
• Bayesian Inference from Case-cohort Data with Multiple End-points
• The central role of the propensity score in observational studies for causal effects
• Causal diagrams for empirical research
• Inference and missing data
• Sample Selection Bias as a Specification Error
• Independence properties of directed markov fields
• Use of Cohort Information in the Design and Analysis of Case‐Control Studies

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