  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.
