插补即一切：估计平均处理效应中的双重稳健性、半参数有效性与自动协变量平衡
Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect
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- University of Washington（华盛顿大学）
- University of California, Berkeley（加州大学伯克利分校）
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中文总结 AI 辅助
本文揭示最小二乘筛子回归插补隐含双重加权结构，通过投影几何实现隐式双重稳健性与半参数有效性，表明插补本身即可获得加权、平衡与稳健性。
中文摘要 AI 辅助
基于插补的因果估计通常被认为完全依赖于结果模型，这与增强逆概率加权的做法形成对比，后者通过拟合两个扰动模型来保护其一致性。本文通过强调最小二乘筛子回归插补中隐藏的双重加权结构，论证了这一观点可能具有误导性。尽管仅显式拟合了结果回归，但由此产生的插补估计量具有精确的加权表示，其诱导权重平衡了筛子空间中的每个函数，且相应的总体权重函数是逆倾向得分在相同筛子空间上的$L^2$投影。这种投影结构产生了一种隐式的双重稳健性，并在标准筛子逼近和增长条件下，实现了具有有效影响函数的渐近线性。因此，加权、协变量平衡、双重稳健性和半参数有效性都可以仅通过插补，借助最小二乘投影的几何结构而涌现。
英文摘要
Imputation-based causal estimation is typically viewed as relying exclusively on an outcome model, in contrast to augmented inverse-probability weighting, whose consistency is protected by fitting two nuisance models. This paper argues that this view can be misleading by highlighting a hidden dual weighting structure in least-squares sieve regression imputation. Although only outcome regressions are explicitly fitted, the resulting imputation estimator admits an exact weighting representation whose induced weights balance every function in the sieve space and the corresponding population weighting functions are the $L^2$ projections of the inverse propensity scores onto the same sieve space. This projection structure yields an implicit form of double robustness and, under standard sieve approximation and growth conditions, asymptotic linearity with the efficient influence function. Thus, weighting, covariate balance, double robustness, and semiparametric efficiency can all emerge from imputation alone through the geometry of least-squares projection.
