genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 19 Feb 2026.
Builds on
20 of 48 references.
References · 20
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
- 2016Generalized Random Forestscited by 312
- 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 219
- 2020Telescoping Density-Ratio Estimationcited by 108
- 2016Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensionscited by 50
- 2020Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimationcited by 45
- 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
- 2016Covariate Balancing Propensity Score by Tailored Loss Functionscited by 34
- 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Pythoncited by 23
- 2023Augmented balancing weights as linear regressioncited by 21
- 2017Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional datacited by 15
8 more
- 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Rcited by 13
- 2025Direct Bias-Correction Term Estimation for Average Treatment Effect Estimationcited by 9
- 2025Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regressioncited by 7
- 2021Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimensioncited by 6
- 2025RieszBoost: Gradient Boosting for Riesz Regressioncited by 6
- 2021Estimating Average Treatment Effects with Support Vector Machinescited by 3
- 2026Generalized Riesz Regression: A Unified Framework for Debiased Machine Learning with Riesz Representer Fitting under Bregman Divergencecited by 3
- 2026Distributional Balancing for Causal Inference: A Unified Framework via Characteristic Function Distancecited by 1
Built on by
Not cited yet.