arXiv 6 Nov 2025 · Statistics — Machine Learning
arXiv:2511.04568 · PDF · DOI · OpenAlex · Extracted main text
Riesz regression has garnered attention as a tool in debiased machine learning for causal and structural parameter estimation (Chernozhukov et al., 2021). This study shows that Riesz regression is closely related to direct density-ratio estimation (DRE) in important cases, including average treat- ment effect (ATE) estimation. Specifically, the idea and objective in Riesz regression coincide with the one in least-squares importance fitting (LSIF, Kanamori et al., 2009) in direct density-ratio estimation. While Riesz regression is general in the sense that it can be applied to Riesz representer estimation in a wide class of problems, the equivalence with DRE allows us to directly import exist- ing results in specific cases, including convergence-rate analyses, the selection of loss functions via Bregman-divergence minimization, and regularization techniques for flexible models, such as neural networks. Conversely, insights about the Riesz representer in debiased machine learning broaden the applications of direct density-ratio estimation methods. This paper consolidates our prior results in Kato (2025a) and Kato (2025b).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2021) Automatic debiased machine learning via riesz regression, 2021 | 1.000 | 5 | 4 | 100% |
| 2 | Masahiro Kato and Takeshi Teshima (2021) Non-negative bregman divergence minimization for deep direct density ratio estimation self | 0.737 | 3 | 2 | 100% |
| 3 | Masahiro Kato (2025) Direct bias-correction term estimation for propensity scores and average treatment effect estimation, 2025a self | 0.737 | 3 | 2 | 100% |
| 4 | Masahiro Kato (2026) A unified framework for debiased machine learning: Riesz representer fitting under bregman divergence, 2026 self | 0.737 | 3 | 2 | 100% |
| 5 | Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori (2011) Density ratio matching under the bregman divergence: A unified framework of density ratio estimation | 0.737 | 3 | 2 | 100% |
| 6 | Jens Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 0.644 | 2 | 2 | 100% |
| 7 | Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama (2009) A least-squares approach to direct importance estimation | 0.644 | 2 | 2 | 100% |
| 8 | Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama (2012) Statistical analysis of kernel-based least-squares density-ratio estimation | 0.644 | 2 | 2 | 100% |
| 9 | Masahiro Kato (2025) Nearest neighbor matching as least squares density ratio estimation and riesz regression, 2025b self | 0.644 | 2 | 2 | 100% |
| 10 | Zhexiao Lin, Peng Ding, and Fang Han (2023) Estimation based on nearest neighbor matching: from density ratio to average treatment effect | 0.644 | 2 | 2 | 100% |
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