Kernel-based covariate functional balancing for observational studies

• 成果类型: Article
• 署名作者: Wong, Raymond K. W.; Chan, Kwun Chuen Gary
• 署名单位: Texas A&M University System; Texas A&M University College Station; University of Washington; University of Washington Seattle
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx069
• 发表日期: 2018
• 页码: 199213
• 关键词: propensity score; Double Robustness; Missing Data; efficient; eigenvalues

摘要

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.