Study design in causal models

Abstract

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions whether a causal or observational relationship can be estimated from the collected incomplete data can be made directly from the graph. Causal models with design offer a systematic and unifying view scientific inference and increase the clarity and speed of communication. Examples on the causal models for a case-control study, a nested case-control study, a clinical trial and a two-stage case-cohort study are presented.

The example on the MORGAM Project extended is in this version

References in corpus (8)

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Cited by in corpus (6)

• Identifying Causal Effects with the R Package causaleffect
• Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
• Correcting for non-ignorable missingness in smoking trends
• Do-search -- a tool for causal inference and study design with multiple data sources
• Simulation Framework for Realistic Large-scale Individual-level Data Generation with an Application in the Health Domain
• Estimating complex causal effects from incomplete observational data