用尼曼正交得分指导去偏机器学习中的平衡方法
Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
- 基于尼曼正交得分设计平衡函数，而非仅依赖协变量
- 处理异质处理效应时，需对完整协变量进行平衡
- 适合因果推断中需要高精度估计的研究者
本文主张，在去偏机器学习中，平衡函数应基于尼曼正交得分构造，而非仅作为协变量的函数。当评分中的回归误差可仅由协变量表示时，协变量平衡有效，这是诸如处理组平均治疗效应（ATT）反事实均值等目标的自然有限维近似。然而，在处理效应异质性下估计平均处理效应（ATE）时，由于结果回归是全协变量 $X=(D,Z)$ 的函数，评分误差通常包含与处理相关的成分。此时，仅平衡 $Z$ 的公共函数会导致处理特定成分未被平衡。因此，我们提倡采用以 $X$ 的基函数进行里茨回归的方式实现回归器平衡，作为DML的一般平衡原则。这并非否定协变量平衡，而是将其视为评分相关回归误差仅为协变量函数这一特殊情况下的适用方法。
原文摘要 · Abstract (English)
This position paper argues that, in debiased machine learning, balancing functions should be derived from the Neyman orthogonal score, not chosen only as functions of covariates. Covariate balancing is effective when the regression error entering the score can be represented by functions of covariates alone, and it is the natural finite-dimensional approximation for targets such as ATT counterfactual means. For ATE estimation under treatment effect heterogeneity, however, the score error generally contains treatment-specific components because the outcome regression is a function of the full regressor $X=(D,Z)$. In that case, balancing common functions of $Z$ can leave the treatment-specific component unbalanced. We therefore advocate regressor balancing, implemented by Riesz regression with basis functions of $X$, as the general balancing principle for DML. The position is not that covariate balancing is invalid, but that covariate balancing should be understood as the special case that is appropriate when the score-relevant regression error is a function of covariates alone.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。