To estimate the effect of an exogenous intervention on a treated unit, such as the implementation of a new criminal justice policy in a given state, a control unit is necessary. Comparing the treated unit’s (let’s say the state of California) time series of the outcome of interest (i.e. violent crime) to the population in which the treated unit is nested (i.e. national time-series) post-intervention would not yield interpretable findings, because it is unknown whether the difference in crime rates was caused by the intervention or some other factor. To navigate this obstacle, California’s crime rates would be compared to a weighted combination of other states chosen to optimally match California’s pre-intervention violent crime trends.
Discrete-Event Simulation Modeling
July 10, 2016 by
A discrete-event simulation (DES) model is a type of queuing model that is widely used to analyze phenomena in demography, logistics, transportation, and operations research. Entities enter a system, compete with other entities for resources, and after a waiting period, exit the system. DES models assume that entities in the model experience the system in a series of discrete “career” stages, and movement from one stage to the next is based on transition probabilities. System models become quite complicated rather quickly, requiring computer simulation in order to produce useful output.