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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A sensitivity analysis using worst-case confidence bounds on trimming bias to assess robustness of causal estimates under limited overlap by measuring required irregularity in the outcome function.
PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.
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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A Sensitivity Approach to Causal Inference Under Limited Overlap
A sensitivity analysis using worst-case confidence bounds on trimming bias to assess robustness of causal estimates under limited overlap by measuring required irregularity in the outcome function.
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PPI++: Efficient Prediction-Powered Inference
PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.