Masahiro Kato
arXiv 30 Oct 2025 · Statistics — Machine Learning
arXiv:2510.26783 · PDF · Extracted main text
This note introduces a unified theory for causal inference that integrates Riesz regression, covariate balancing, density-ratio estimation (DRE), targeted maximum likelihood estimation (TMLE), and the matching estimator in average treatment effect (ATE) estimation. In ATE estimation, the balancing weights and the regression functions of the outcome play important roles, where the balancing weights are referred to as the Riesz representer, bias-correction term, and clever covariates, depending on the context. Riesz regression, covariate balancing, DRE, and the matching estimator are methods for estimating the balancing weights, where Riesz regression is essentially equivalent to DRE in the ATE context, the matching estimator is a special case of DRE, and DRE is in a dual relationship with covariate balancing. TMLE is a method for constructing regression function estimators such that the leading bias term becomes zero. Nearest Neighbor Matching is equivalent to Least Squares Density Ratio Estimation and Riesz Regression.
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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 | Masahiro Kato (2025) Direct debiased machine learning via bregman divergence minimization, 2025b self | 1.000 | 8 | 4 | 100% |
| 2 | Masahiro Kato (2025) Nearest neighbor matching as least squares density ratio estimation and riesz regression, 2025c self | 0.928 | 4 | 4 | 100% |
| 3 | Masahiro Kato (2025) Direct bias-correction term estimation for propensity scores and average treatment effect estimation, 2025a self | 0.843 | 3 | 3 | 100% |
| 4 | David Bruns-Smith, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn (2025) Augmented balancing weights as linear regression | 0.737 | 3 | 2 | 100% |
| 5 | Jens Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 0.737 | 3 | 2 | 100% |
| 6 | Qingyuan Zhao (2019) Covariate balancing propensity score by tailored loss functions | 0.644 | 4 | 1 | 100% |
| 7 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2024) Automatic debiased machine learning via riesz regression, 2024 | 0.511 | 2 | 1 | 100% |
| 8 | Victor Chernozhukov, Whitney K. Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.405 | 1 | 1 | 100% |
| 9 | Daniel G. Horvitz and Donovan J. Thompson (1952) A generalization of sampling without replacement from a finite universe | 0.405 | 1 | 1 | 100% |
| 10 | Kosuke Imai and Marc Ratkovic (2013) Estimating treatment effect heterogeneity in randomized program evaluation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.