SHIFT combines cross-fit DML with kernel-local Welsch loss optimized via Graduated Non-Convexity and a MAD-scaled defensive OLS refit to achieve robust average dose-response estimation under localized heavy-tailed contamination while recovering outlier masks.
Doublemldeep: Estimation of causal effects with multimodal data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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Treatment policies from multimodal EHRs improve when doubly robust pseudo-outcomes are built from annotated confounders and then regressed onto text-and-tabular representations, rather than estimating effects directly from those representations.
citing papers explorer
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SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
SHIFT combines cross-fit DML with kernel-local Welsch loss optimized via Graduated Non-Convexity and a MAD-scaled defensive OLS refit to achieve robust average dose-response estimation under localized heavy-tailed contamination while recovering outlier masks.
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Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records
Treatment policies from multimodal EHRs improve when doubly robust pseudo-outcomes are built from annotated confounders and then regressed onto text-and-tabular representations, rather than estimating effects directly from those representations.