arXiv 19 Feb 2026 · Statistics — Machine Learning
arXiv:2602.17543 · PDF · DOI · OpenAlex · Extracted main text
Efficient estimation of causal and structural parameters can be automated using the Riesz representation theorem and debiased machine learning (DML). We present genriesz, an open-source Python package that implements automatic DML and generalized Riesz regression, a unified framework for estimating Riesz representers by minimizing empirical Bregman divergences. This framework includes covariate balancing, nearest-neighbor matching, calibrated estimation, and density ratio estimation as special cases. A key design principle of the package is automatic regressor balancing (ARB): given a Bregman generator $g$ and a representer model class, genriesz} automatically constructs a compatible link function so that the generalized Riesz regression estimator satisfies balancing (moment-matching) optimality conditions in a user-chosen basis. The package provides a modulr interface for specifying (i) the target linear functional via a black-box evaluation oracle, (ii) the representer model via basis functions (polynomial, RKHS approximations, random forest leaf encodings, neural embeddings, and a nearest-neighbor catchment basis), and (iii) the Bregman generator, with optional user-supplied derivatives. It returns regression adjustment (RA), Riesz weighting (RW), augmented Riesz weighting (ARW), and TMLE-style estimators with cross-fitting, confidence intervals, and $p$-values. We highlight representative workflows for estimation problems such as the average treatment effect (ATE), ATE on treated (ATT), and average marginal effect estimation. The Python package is available at https://github.com/MasaKat0/genriesz and on PyPI.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Masahiro Kato (2026) A unified framework for debiased machine learning: Riesz representer fitting under bregman divergence, 2026 self | 0.811 | 4 | 2 | 100% |
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| 4 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2021) Automatic debiased machine learning via riesz regression, 2021 | 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 | Philipp Bach, Victor Chernozhukov, Malte S. Kurz, and Martin Spindler (2022) Doubleml - an object-oriented implementation of double machine learning in python | 0.644 | 2 | 2 | 100% |
| 7 | Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, M… (2019) EconML: A Python package for ML-based heterogeneous treatment effects estimation | 0.644 | 2 | 2 | 100% |
| 8 | Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Z… (2020) CausalML: Python package for causal machine learning, 2020 | 0.644 | 2 | 2 | 100% |
| 9 | 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% |
| 10 | Koji Makiyama (2019) densratio: Density ratio estimation | 0.644 | 2 | 2 | 100% |
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