Riesz regression as direct density ratio estimation
Abstract
This study clarifies the relationship between Riesz regression [Chernozhukov et al., 2021] and density ratio estimation (DRE) in causal inference problems, such as average treatment effect estimation. We first show that the Riesz representer can be written as a signed density ratio and then demonstrate that the Riesz regression objective coincides with the least-squares importance fitting criterion [Kanamori et al., 2009]. Although Riesz regression applies to a broad class of representer estimation problems, this equivalence with DRE allows us to transfer existing DRE results, including convergence rate analyses, generalizations based on Bregman divergence minimization, and regularization techniques for flexible models such as neural networks.
Fields of Study & Subjects
Explainable Artificial Intelligence (XAI)
Advanced Statistical Modeling Techniques
Advanced Causal Inference Techniques
Publication details
- Journal / source
- Finance research letters
- Publisher
- Elsevier BV
- Volume
- 112
- Pages
- 110550–110550
- Publication date
- 1 August 2026
- Language
- EN