Statistical matching is a technique which can be applied when one wants to investigate the joint relationship between two variables that are observed in different datasets, using one or more...Show moreStatistical matching is a technique which can be applied when one wants to investigate the joint relationship between two variables that are observed in different datasets, using one or more variables that overlap in both datasets. This joint relationship cannot be estimated without relying on assumptions or additional data. Classically, statistical matching is based on the Conditional Independence Assumption (CIA) which asserts the non-overlapping variables to be independent given the overlapping variable. This assumption is inflexible, untestable and often does not hold. The current project proposes to use an approach based on the Instrumental Variable Assumption (IVA). An instrumental variable is a variable that, given the value of some mediating variable, has no effect on some outcome variable. In the context of statistical matching this gives rise to three scenarios: the mediating variable overlaps, the outcome variable overlaps, or the instrumental variable overlaps. The IVA approach is more flexible than the CIA approach. This is because the IVA approach does not make any assumptions on which variable is the overlapping variable, whereas the CIA always conditions on the overlapping variable. The aims of the current study were twofold: 1) how does the IVA approach perform when the assumption is violated to various degrees and 2) how does the IVA approach compare to the CIA approach. To answer these questions, a simulation study was performed. For each scenario, joint probabilities of the non-overlapping variables were estimated under both the IVA and the CIA in populations which violate the IVA to various degrees. Measures for the bias, accuracy and precision were estimated and compared. The results indicate that the IVA approach is moderately robust against slight violations of the assumption. When the IVA is not violated, estimations are unbiased and for all matching scenarios the method outperforms the CIA. When the IVA is violated it is advisable to rely on the CIA, since results of the current simulation study suggest the CIA to be more robust against violations in general.Show less
