2021/01/10 by Xialing Wen, Wen, Xialing, Ying Yan +5
Mathematics · #Advanced Causal Inference Techniques #Artificial intelligence #Computer science #Confounding #Covariate #Data mining #Econometrics #FOS: Computer and information sciences #Kernel (algebra) #Mathematics #Methodology (stat.ME) #Observational study #Preprocessor #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #stat.ME
paper · pdf · doi:10.48550/arxiv.2101.03463
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/01/10 · openalex publication_date 2021/01/10 · arxiv updated 2021/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A common concern in observational studies focuses on properly evaluating the causal effect, which usually refers to the average treatment effect or the average treatment effect on the treated. In this paper, we propose a data preprocessing method, the Kernel-distance-based covariate balancing, for observational studies with binary treatments. This proposed method yields a set of unit weights for the treatment and control groups, respectively, such that the reweighted covariate distributions can satisfy a set of pre-specified balance conditions. This preprocessing methodology can effectively reduce confounding bias of subsequent estimation of causal effects. We demonstrate the implementation and performance of Kernel-distance-based covariate balancing with Monte Carlo simulation experiments and a real data analysis.