Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
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Partial identification of principal causal effects under violations of principal ignorability
Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.