Masahiro Kato
arXiv 27 Oct 2025 · Econometrics
arXiv:2510.23534 · PDF · Extracted main text
We develop a direct debiased machine learning framework comprising Neyman targeted estimation and generalized Riesz regression. Our framework unifies Riesz regression for automatic debiased machine learning, covariate balancing, targeted maximum likelihood estimation (TMLE), and density-ratio estimation. In many problems involving causal effects or structural models, the parameters of interest depend on regression functions. Plugging regression functions estimated by machine learning methods into the identifying equations can yield poor performance because of first-stage bias. To reduce such bias, debiased machine learning employs Neyman orthogonal estimating equations. Debiased machine learning typically requires estimation of the Riesz representer and the regression function. For this problem, we develop a direct debiased machine learning framework with an end-to-end algorithm. We formulate estimation of the nuisance parameters, the regression function and the Riesz representer, as minimizing the discrepancy between Neyman orthogonal scores computed with known and unknown nuisance parameters, which we refer to as Neyman targeted estimation. Neyman targeted estimation includes Riesz representer estimation, and we measure discrepancies using the Bregman divergence. The Bregman divergence encompasses various loss functions as special cases, where the squared loss yields Riesz regression and the Kullback-Leibler divergence yields entropy balancing. We refer to this Riesz representer estimation as generalized Riesz regression. Neyman targeted estimation also yields TMLE as a special case for regression function estimation. Furthermore, for specific pairs of models and Riesz representer estimation methods, we can automatically obtain the covariate balancing property without explicitly solving the covariate balancing objective.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama (2009) A least-squares approach to direct importance estimation | 1.000 | 9 | 5 | 100% |
| 2 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2024) Automatic debiased machine learning via riesz regression, 2024 | 1.000 | 8 | 6 | 100% |
| 3 | Qingyuan Zhao (2019) Covariate balancing propensity score by tailored loss functions | 1.000 | 8 | 5 | 100% |
| 4 | Jens Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 1.000 | 5 | 3 | 100% |
| 5 | José R. Zubizarreta (2015) Stable weights that balance covariates for estimation with incomplete outcome data | 0.928 | 4 | 3 | 100% |
| 6 | Victor Chernozhukov, Whitney K. Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.874 | 6 | 2 | 100% |
| 7 | Masahiro Kato and Takeshi Teshima (2021) Non-negative bregman divergence minimization for deep direct density ratio estimation self | 0.843 | 3 | 3 | 100% |
| 8 | David Bruns-Smith, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn (2025) Augmented balancing weights as linear regression | 0.811 | 4 | 2 | 100% |
| 9 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.811 | 4 | 2 | 100% |
| 10 | Kosuke Imai and Marc Ratkovic (2013) Estimating treatment effect heterogeneity in randomized program evaluation | 0.737 | 3 | 2 | 100% |
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