论文标题
目标功能平衡
Targeted Function Balancing
论文作者
论文摘要
本文介绍了靶向功能平衡（TFB），这是一种协变量平衡权重框架，用于估计二进制干预的平均治疗效果。 TFB首先会在协变量上回归结果，然后选择平衡功能（协变量）的权重，而该功能在概率上近似于所得的回归函数。这在回归函数的预测值和协变量中产生平衡，而回归函数的估计方差决定了协变量中的平衡足够。值得注意的是，TFB表明，有意在某些协变量中留下失衡可以提高效率而不引入偏见，这挑战了警告任何变量中不平衡的传统。此外，TFB完全由回归函数及其估计方差定义，这将如何最好地平衡协变量的问题变成了如何最好地模拟结果。内核正规化最小二乘（KRLS），Lasso和贝叶斯添加期回归树（BART）被认为是回归估计器。使用KRL，TFB有助于基于内核的文献。至于LASSO，TFB使用回归函数的估计方差来优先在协变量的某些维度下平衡，这一功能可以通过选择稀疏的回归估计器来大大利用。对于BART，我们证明TFB可以应用没有线性表示的回归估计器。 R软件包TFB实现了TFB。
This paper introduces Targeted Function Balancing (TFB), a covariate balancing weights framework for estimating the average treatment effect of a binary intervention. TFB first regresses an outcome on covariates, and then selects weights that balance functions (of the covariates) that are probabilistically near the resulting regression function. This yields balance in the regression function's predicted values and the covariates, with the regression function's estimated variance determining how much balance in the covariates is sufficient. Notably, TFB demonstrates that intentionally leaving imbalance in some covariates can increase efficiency without introducing bias, challenging traditions that warn against imbalance in any variable. Additionally, TFB is entirely defined by a regression function and its estimated variance, turning the problem of how best to balance the covariates into how best to model the outcome. Kernel regularized least squares (KRLS), the LASSO, and Bayesian Additive Regression Trees (BART) are considered as regression estimators. With KRLS, TFB contributes to the literature of kernel-based weights. As for the LASSO, TFB uses the regression function's estimated variance to prioritize balance in certain dimensions of the covariates, a feature that can be greatly exploited by choosing a sparse regression estimator. With BART, we demonstrate that TFB can apply regression estimators that do not have linear representations. The R Package tfb implements TFB.
