Kernel-based covariate functional balancing for observational studies

Raymond K W Wong and Kwun Chuen Gary Chan

Biometrika, 2018, vol. 105, issue 1, 199-213

Abstract: Summary 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.

Keywords: Average treatment effect; Eigenvalue optimization; Reproducing-kernel Hilbert space; Sobolev space (search for similar items in EconPapers)
Date: 2018
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