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基于核的观察性研究协变量函数平衡法

Kernel-based covariate functional balancing for observational studies.

作者信息

Wong Raymond K W, Chan Kwun Chuen Gary

机构信息

Department of Statistics, Texas A&M University, 401E Blocker Building, 155 Ireland Street, College Station, Texas 77843, U.S.A.

Department of Biostatistics, University of Washington, 1959 NE Pacific St., Seattle, Washington 98195, U.S.A.

出版信息

Biometrika. 2018 Mar;105(1):199-213. doi: 10.1093/biomet/asx069. Epub 2017 Dec 8.

Abstract

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.

摘要

协变量平衡常被提倡用于客观因果推断,因为它能在观测数据中模拟随机化。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了协变量函数的一致近似平衡。相应的无限维优化问题被证明在特征值优化问题方面具有有限维表示。研究了大样本结果,数值示例表明,与现有方法相比,所提方法以更小的抽样变异性实现了更好的平衡。

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