Efficient Covariate Balancing for the Local Average Treatment Effect
针对二元工具变量下的双向不依从问题，提出一种通过逆概率加权实现协变量精确平衡的方法，无需对结果或处理选择做函数形式假设，相比传统逆概率加权法偏差更低、方差更小，并给出了渐近正态性和半参数效率的条件。
This article develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to impose exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the resulting treatment effect estimates are automatically weight normalized and exhibit both low bias and reduced variance in finite samples compared to conventional inverse probability weighting methods. We provide conditions for asymptotic normality and semiparametric efficiency and demonstrate how to use additional information about the treatment selection step for bias reduction in finite samples. A doubly robust extension is proposed as well. Monte Carlo simulations suggest that the theoretical advantages translate well to finite samples. The method is illustrated in an empirical example.