  We develop a general dynamical model as a framework for possible causal
interpretation. We first state a criterion of local independence in terms of
measurability of processes involved in the Doob-Meyer decomposition of
stochastic processes, as in Aalen (1987); then we define direct and indirect
influence. We propose a definition of causal influence using the concepts of
``physical system''. This framework makes it possible to link descriptive and
explicative statistical models, and encompasses quantitative processes and
events. One of the features of this paper is the clear distinction between the
model for the system and the model for the observation. We give a dynamical
representation of a conventional joint model for HIV load and CD4 counts. We
show its inadequacy to capture causal influences while on the contrary known
mechanisms of HIV infection can be expressed directly through a system of
differential equations.
