English(EN) When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

因果推断研究质疑预测误差作为唯一性能指标

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

一篇新研究论文探讨了在因果推断中使用预测误差来评估干扰函数估计器的局限性。该研究模拟了部分线性模型，比较了OLS、GAMs、XGBoost和DML-XGBoost等方法。结果表明，虽然XGBoost在非oracle方法中显示出最低的RMSE，并且DML-XGBoost提供了更好的置信区间覆盖率，但预测误差与因果偏差或置信区间覆盖质量并不总是相关。研究表明，预测误差对于评估干扰函数估计是有用的，但不应作为因果估计器性能的唯一指标。 AI

影响强调了在简单预测误差之外，对因果推断方法进行细致评估的必要性。

排序理由关于因果推断方法的学术论文。

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arXiv cs.AI TIER_1 English(EN) · Cong Cao · 2026-09-02 04:00

当预测误差不足以衡量因果估计中的干扰函数预测时

arXiv:2609.00071v1 Announce Type: new Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance may differ across performance measures. We studied this question in a partially lin…

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当预测误差不足以衡量因果估计中的干扰函数预测时

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