Researchers have developed a new method for addressing epistemic uncertainty in machine learning models, particularly when training data is subject to selection bias. The proposed technique adapts the Heckman correction, originally from econometrics, to jointly model the selection process and the outcome, correcting for unobserved variables that influence both. Experiments show that standard methods like importance weighting fail to maintain accurate confidence intervals when selection bias is high, whereas the Heckman-corrected approach significantly improves calibration. AI
IMPACT This research could lead to more reliable confidence intervals in ML models trained on biased data, improving decision-making in fields like finance and healthcare.
RANK_REASON The cluster contains a research paper published on arXiv detailing a novel methodology for machine learning.
- arXiv
- deep epistemic uncertainty
- deep outcome network
- genetic programming
- Heckman
- Heckman's two-equation model
- inverse Mills ratio
- MC Dropout
- probit
- Stata
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