Outcome Based Matching

2017/12/14 by Jonathan Bates, Bates, Jonathan, Alexander Cloninger +1
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management

paper · pdf · doi:10.48550/arxiv.1712.05063

openalex publication_date 2017/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a method to reduce variance in treatment effect estimates in the setting of high-dimensional data. In particular, we introduce an approach for learning a metric to be used in matching treatment and control groups. The metric reduces variance in treatment effect estimates by weighting covariates related to the outcome and filtering out unrelated covariates.

Citations

• Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
• The central role of the propensity score in observational studies for causal effects
• Variable Selection for Propensity Score Models

Related

• Matching Using Sufficient Dimension Reduction for Causal Inference
• Interpretable Almost Matching Exactly for Causal Inference
• Matching-Based Nonparametric Estimation of Group Average Treatment Effects
• Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics
• Demystifying Statistical Matching Algorithms for Big Data