Some Cautions Concerning The Application Of Causal Modeling Methods

TLDR

Abstract

Abstract Literal acceptance of the results of fitting "causal" models to correlational data can lead to conclusions that are of questionable value. The long-established principles of scientific inference must still be applied. In particular, the possible influence of variables that are not observed must be considered; the well-known difference between correlation and causation is still relevant, even when variables are separated in time; the distinction between measured variables and their theoretical counterparts still exists; and ex post facto analyses are not tests of models. There seems to be some danger of overlooking these principles when complex computer programs are used to analyze. correlational data, even though these new methods provide great increases in the rigor with which correlational data can be analyzed.