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Updated: Mar 27, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Some Cautions Concerning The Application Of Causal Modeling Methods.

N Cliff

    Multivariate Behavioral Research
    |January 15, 2016
    PubMed
    Summary

    Researchers caution against over-interpreting "causal" findings from correlational data. Applying established scientific inference principles, like considering unobserved variables, remains crucial for valid conclusions, even with advanced statistical methods.

    Area of Science:

    • Statistics
    • Scientific Inference
    • Data Analysis

    Background:

    • Correlational data analysis is increasingly sophisticated.
    • Complex statistical models are often used to infer causality.
    • Misinterpretation of correlational findings can lead to flawed conclusions.

    Purpose of the Study:

    • To emphasize the continued importance of fundamental scientific inference principles.
    • To caution against the literal acceptance of causal conclusions from correlational data.
    • To highlight potential pitfalls when using advanced computational methods for data analysis.

    Main Methods:

    • Review of established principles of scientific inference.
    • Discussion of the distinction between correlation and causation.

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  • Consideration of the influence of unobserved variables.
  • Critique of ex post facto analyses as model tests.

    • Main Results:

    • Literal acceptance of "causal" models fitted to correlational data can yield questionable results.
    • The difference between correlation and causation remains relevant, irrespective of temporal separation of variables.
    • The distinction between measured variables and theoretical constructs persists.
    • Ex post facto analyses are not valid tests of models.

    Conclusions:

    • Fundamental principles of scientific inference must be applied to correlational data analysis.
    • Unobserved variables and the correlation-causation distinction are critical considerations.
    • Advanced computational methods enhance analytical rigor but do not negate the need for sound inferential principles.