stat.MLFeb 19, 2026

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

Authors: Masahiro Kato

Organizations: Data Analytics Department, Mizuho-DL Financial Technology, Co., Ltd.

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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 gg 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 pp-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.

Figures & tables

Figure 1: Flowchart of genriesz .
g(α)g(\alpha)DR estimationRR estimation
(α−C)2\left(\alpha-C\right)^{2}LSIFSQ-Riesz regression
( Kanamori et al., 2009 )( genriesz )
 KuLSIFRiesz regression (RieszNet and ForestRiesz)
 ( Kanamori et al., 2012 )( Chernozhukov et al., 2021 ; Chernozhukov et al., 2022a )
 Hyvärinen score matchingRieszBoost
 ( Hyvärinen, 2005 )( Lee & Schuler, 2025 )
Table 1: Correspondence among Bregman divergence losses, density ratio (DR) estimation methods, and Riesz representer (RR) estimation. RR estimation for ATE includes propensity score estimation and covariate balancing weights. In the table, C∈RC\in{\mathbb{R}} denotes a constant that is determined by the problem and the loss function. The package also supports user-defined generators.
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