Masahiro Kato
arXiv 28 Oct 2025 · Econometrics
arXiv:2510.24433 · PDF · Extracted main text
This study proves that Nearest Neighbor (NN) matching can be interpreted as an instance of Riesz regression for automatic debiased machine learning. Lin et al. (2023) shows that NN matching is an instance of density-ratio estimation with their new density-ratio estimator. Chernozhukov et al. (2024) develops Riesz regression for automatic debiased machine learning, which directly estimates the Riesz representer (or equivalently, the bias-correction term) by minimizing the mean squared error. In this study, we first prove that the density-ratio estimation method proposed in Lin et al. (2023) is essentially equivalent to Least-Squares Importance Fitting (LSIF) proposed in Kanamori et al. (2009) for direct density-ratio estimation. Furthermore, we derive Riesz regression using the LSIF framework. Based on these results, we derive NN matching from Riesz regression. This study is based on our work 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 | Zhexiao Lin, Peng Ding, and Fang Han (2023) Estimation based on nearest neighbor matching: from density ratio to average treatment effect | 1.000 | 23 | 5 | 100% |
| 2 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2024) Automatic debiased machine learning via riesz regression, 2024 | 1.000 | 5 | 3 | 100% |
| 3 | Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama (2009) A least-squares approach to direct importance estimation | 0.928 | 4 | 3 | 100% |
| 4 | Masahiro Kato (2025) Direct bias-correction term estimation for propensity scores and average treatment effect estimation, 2025a self | 0.843 | 3 | 3 | 100% |
| 5 | Masahiro Kato (2025) Direct debiased machine learning via bregman divergence minimization, 2025b self | 0.843 | 3 | 3 | 100% |
| 6 | Alberto Abadie and Guido W. Imbens (2006) Large sample properties of matching estimators for average treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama (2012) Statistical analysis of kernel-based least-squares density-ratio estimation | 0.585 | 3 | 1 | 100% |
| 8 | Alberto Abadie and Guido W. Imbens (2011) Bias-corrected matching estimators for average treatment effects | 0.405 | 1 | 1 | 100% |
| 9 | David Bruns-Smith, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn (2025) Augmented balancing weights as linear regression | 0.405 | 1 | 1 | 100% |
| 10 | Victor Chernozhukov, Whitney K. Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.