Graphical models for inference under outcome-dependent sampling

DOI10.1214/10-STS340zbMath1329.62042arXiv1101.0901OpenAlexW3099830989MaRDI QIDQ906534

Niels Keiding, Vanessa Didelez, Svend Kreiner

Publication date: 22 January 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1101.0901

zbMATH Keywords

collapsibilityselection biascausal inferenceodds ratios

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Sampling theory, sample surveys (62D05) Graphical methods in statistics (62A09)

Related Items

Causal Bounds for Outcome-Dependent Sampling in Observational Studies, Adjustments and their Consequences-Collapsibility Analysis using Graphical Models, The directions of selection bias, On the logic of collapsibility for causal effect measures, Multivariate functional principal component analysis for endogenously stratified data, Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview, Simple strategies for semi-supervised feature selection, Estimating and contextualizing the attenuation of odds ratios due to non collapsibility, Study Design in Causal Models, Causal diagrams for interference

Uses Software

• TETRAD

Cites Work

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