Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods.

中文翻译：

---

用于观察性研究的基于内核的协变量功能平衡

协变量平衡经常被提倡用于客观因果推断，因为它模仿观察数据中的随机化。与平衡协变量特定矩的方法不同，我们的提议在再生核希尔伯特空间中实现协变量函数的统一近似平衡。根据特征值优化问题，相应的无限维优化问题显示为具有有限维表示。对大样本结果进行了研究，数值例子表明，与现有方法相比，所提出的方法在较小的采样变异性下实现了更好的平衡。