dScoreTest: Debiased Score Tests for Goodness of Fit and Model Comparison
Debiased (Neyman-orthogonalized) score tests for assessing
whether a semiparametric or parametric regression model is well-specified
and for comparing nested models. The test employs a hunt-and-test strategy:
on a held-out hunt sample, it fits the null model and uses machine
learning to find a direction in which the null model's score seems positive;
on an independent test sample, it assesses the significance of the score in
the hunted direction. The test employs orthogonalization to eliminate the
bias from estimating the null model, yielding a test statistic that is
asymptotically standard normal under the null without requiring a parametric
form for the alternative. Methods are provided for 'glm', 'lm' and
'mgcv::gam' fits as well as for detecting heterogeneous treatment effects.
The methodology is described in Dhawan, Guo and Shah (2026)
<doi:10.48550/arXiv.2607.28861>.
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[aut, cre, cph],
Aditya Dhawan [aut]