Targeted Function Balancing: Flexible balancing weights that leverage strategic imbalance to estimate causal effects
目标函数平衡：利用策略性不平衡估计因果效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究二元干预的平均处理效应，以协变量平衡权重处理观察数据中的效应估计问题，文本依据对应因果推断。
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目标函数平衡：利用策略性不平衡估计因果效应
- 研究问题
- 用权重估计二元处理的平均效应时，如何决定哪些协变量需要严格平衡，哪些不平衡可以保留，以兼顾偏差与效率？
- 方法与思路
- 作者先拟合结果回归，再平衡概率意义上接近该回归的协变量函数，以回归预测及其估计方差决定协变量平衡的程度。
- 材料陈述的贡献
- 摘要称，TFB 可优先平衡影响结果的重要协变量，并在所研究条件下通过保留部分不平衡提高效率而不引入偏差。
- 阅读关注点
- 应核对平衡目标、回归方差的估计方式及无偏差结论的适用条件，并比较不同结果模型对权重、稳定性和效应估计的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出因果识别与理论条件，不能把允许不平衡理解为任意变量均可忽略。
原文摘要
When estimating the average treatment effect of a binary intervention with weights, investigators are often burdened with how best to leave imbalance in the distributions of observed covariates. This requires that investigators proceed cautiously, as imbalance in key (nonlinear transformations of) covariates potentially introduces bias into the ultimate estimated effect. This paper introduces Targeted Function Balancing (TFB), a flexible covariate balancing weights approach that simplifies this problem for the researcher. 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. Though, TFB still does prioritize balance in the covariates that are most influential on the outcome. 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, the LASSO, and Bayesian Additive Regression Trees are considered as regression estimators. We apply TFB to estimate the effects of taking mathematics courses in twelfth grade in the Los Angeles Unified School District. The R Package tfb implements TFB.