Study design in causal models

TL;DR

This paper discusses the importance of explicit study design in causal models, emphasizing how integrating design and causal assumptions enhances clarity, inference accuracy, and communication in empirical research.

Contribution

It introduces causal models with design that incorporate study design, missing data mechanisms, and causal structure for improved causal inference and communication.

Findings

Causal models with design unify study assumptions and causal structure.

They enable direct assessment of estimability from incomplete data.

Examples demonstrate practical applications in various study types.

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.