Hypothesis Formulation, Model Interpretation, and Model Equivalence: Implications of a Mereological Causal Interpretation of Structural Equation Models.

One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts determines the state of the whole. This interpretation shares several properties with efficient causal interpretations but also differs in terms of other important properties. The availability of alternative causal interpretations of the same statistical models has implications for hypothesis specification, research design, causal inference, data analysis, and the interpretation of research results.