Weighted Euclidean balancing for a matrix exposure in estimating causal effect
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矩阵型暴露因果效应估计中的加权欧氏平衡
- 研究问题
- 处理变量为矩阵、协变量平衡约束数量庞大时，如何避免精确平衡困难与近似平衡阈值选择问题，并估计因果效应？
- 方法与思路
- 作者提出从整体角度近似平衡协变量的加权欧氏方法，涵盖高维协变量，并构造参数与非参数两类矩阵处理效应估计。
- 材料陈述的贡献
- 摘要称提供两类估计的理论性质，并在模拟中优于所比较方法；培训课程应用报告特定实践课程时长与儿童智商的正向效应。
- 阅读关注点
- 应核对矩阵暴露的效应定义、识别与支持条件、平衡目标及调参方式，并检查模拟基准和课程应用中的混杂处理。
- 阅读边界
- 所给来源为 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明理论条件，模拟优势不能直接外推。
原文摘要
In many scientific fields such as biology, psychology and sociology, there is an increasing interest in estimating the causal effect of a matrix exposure on an outcome. Covariate balancing is crucial in causal inference and both exact balancing and approximate balancing methods have been proposed in the past decades. However, due to the large number of constraints, it is difficult to achieve exact balance or to select the threshold parameters for approximate balancing methods when the treatment is a matrix. To meet these challenges, we propose the weighted Euclidean balancing method, which approximately balance covariates from an overall perspective. This method is also applicable to high-dimensional covariates scenario. Both parametric and nonparametric methods are proposed to estimate the causal effect of matrix treatment and theoretical properties of the two estimations are provided. Furthermore, the simulation results show that the proposed method outperforms other methods in various cases. Finally, the method is applied to investigating the causal relationship between children's participation in various training courses and their IQ. The results show that the duration of attending hands-on practice courses for children at 6-9 years old has a siginificantly positive impact on children's IQ.