Abstract
Event history analysis is closely related to a new understanding of causation as a “generative process.” Event history models allow the researcher (1) to relate the rate of change in future outcomes to changes in conditions in the past; (2) to use the transition rate as a local, time‐related description of how the dependent process in a causal system evolves in time; and (3) to use time‐dependent covariates in order to study the impact of parallel processes and time lags between causes and their effects as well as different temporal effect shapes. Depending on the precision of the time measurement of events, discrete‐time and continuous‐time event history methods can be distinguished.
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