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    LoyalLoyalJusticeJustice
    Made withinDC&Austin
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    Home/Original/inverse
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    Inverse View

    It is not the case that Variance decomposition need not presuppose additivity; interaction components (G×E) can themselves be quantified and assigned proportional weight.

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    Reasons For

    1 perspective
    Reason for
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    • 1.Interaction variance is context-dependent and scale-dependent; assigning fixed proportional weight conflates mathematical possibility with conceptual meaning.
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    • 2.Without additivity, partitioning becomes arbitrary—different decomposition schemes yield different G×E weights for identical data, undermining objectivity.
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    • 3.The claim conflates quantification with interpretation; we can compute G×E terms without establishing they merit independent causal or explanatory weight.
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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Interaction effects are empirically real phenomena that influence outcomes; excluding them from decomposition artificially obscures causal structure.
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    • 2.Proportional weighting of G×E terms is mathematically coherent via variance partitioning methods that don't require linear additivity assumptions.
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    • 3.Many biological systems exhibit genuine non-additive effects; additivity-based models misrepresent these systems' actual organizational logic.
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