The Cross-Lagged Correlation Technique: Reconsideration in Terms of Exploratory Utility, Assumption Specification and Robustness

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Abstract
The cross-lagged correlation technique (CLC) for assessing causality from passive observational data has been the subject of much recent criticism. CLC compares cross correlations between variables across time points of measurement and attributes differences in correlations to causal effects. Some of the major areas of criticism have to do with: (a) lack of a no-cause baseline, (b) spurious effects of mediating variables, and (c) obscuring effects of heterogeneous stabilities. The present author attempts to show that while these criticisms are valid, CLC should not be rejected outright as a tool in social science research. CLC has some utility as an exploratory technique. Furthermore, if certain assumptions are specified and these assumptions are correct, valid inferences can be made from CLC. Lastly, some assumptions for CLC are not as stringent as implied by critics and CLC is robust to minor violations of assumptions.

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