PRCD-MAP assigns per-edge trust to imperfect priors in causal discovery via empirical Bayes calibration and MLP propagation, delivering an ε-safety guarantee that vanishes at prior-quality extremes and empirical gains on CausalTime datasets.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.
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PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
PRCD-MAP assigns per-edge trust to imperfect priors in causal discovery via empirical Bayes calibration and MLP propagation, delivering an ε-safety guarantee that vanishes at prior-quality extremes and empirical gains on CausalTime datasets.
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Post-Screening Portfolio Selection
A Lasso-based screening step followed by low-dimensional mean-variance optimization on the selected assets improves high-dimensional portfolio construction, with a defactoring extension for strong factors.