ABSTRACT
This chapter aims to develop a more systematic sketch of how causal relations suggested by a dynamic combination of macro and micro theories can be represented in event history models and then possibly be better examined with temporal data. Since all causal propositions have consequences for longitudinal change, only time-changing variables provide more convincing empirical evidence of causal relations. The role of a time-dependent covariate in the approach is to indicate that a causal factor has changed its state at a specific time and that the unit under study is exposed to another causal condition. Event history analysis provides effective tools to test causal propositions derived from a dynamic combination of micro- and macro-level considerations. A correct causal interpretation of a typical action means that the process which is claimed to be typical is shown to be both adequately grasped on the level of meaning and the interpretation is to some degree causally adequate.
