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.
In our main sweep, p≤0.25, so this is out of scope but worth flagging for deployment
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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.