The authors define screening collapse as the progressive erosion of the active–inactive score margin under recursive synthetic labels, causing marginal screeners to omit features that clean-label screening would retain.
Recursive synthetic data can cause screening to discard relevant variables before model fitting
Zhang and Chen formalize screening collapse in Gaussian linear models, showing that omitted variables incur an irreducible loss not recoverable by subsequent model fitting.
Academic
Jia Zhang · Xin Chen
Southwestern University of Finance and Economics · Southern University of Science and Technology
Research Digest··3 min read
The authors introduce and analyze screening collapse, a phenomenon where recursive synthetic data (labels generated by previous models) degrades variable screening, causing relevant features to be discarded.
Why this paper
From Southwestern University of Finance and Economics and Southern University of Science and Technology
In one line
Recursive synthetic labels cause variable screening to discard features that clean-label screening would retain.
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