RIIM: Randomization-Based Inference Under Inexact Matching
Jianan Zhu, Jeffrey Zhang, Zijian Guo, Siyu Heng · 2025
Randomization-based inference for average treatment effects in potentially inexactly matched observational studies. It implements the inverse post-matching probability weighting framework proposed by the authors. The post-matching probability calculation follows the approach of Pimentel and Huang (2024) . The optimal full matching method is based on Hansen (2004) . The variance estimator extends the method proposed in Fogarty (2018) from the perfect randomization settings to the potentially inexact matching case. Comparisons are made with conventional methods, as described in Rosenbaum (2002) , Fogarty (2018) , and Kang et al. (2016) .