genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression

• URL: http://arxiv.org/abs/2602.17543v1
• Date: Thu, 19 Feb 2026 16:58:40 GMT
• Title: genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
• Authors: Masahiro Kato,
• Abstract summary: We present genriesz, an open-source Python package that implements automatic DML and generalized Riesz regression.<n>genriesz automatically constructs a compatible link function so that the generalized Riesz regression estimator satisfies balancing (moment-matching) optimality conditions.
• Score: 6.44705221140412
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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