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

2026-02-19

AI Summary

This work proposes a unified framework based on generalized Riesz regression to address the challenges of estimating causal and structural parameters under high-dimensional confounding and potential model misspecification. By minimizing an empirical Bregman divergence, the method automatically constructs estimators that satisfy optimal moment-matching conditions and introduces an Automatic Regressor Balancing (ARB) mechanism. This mechanism adaptively generates compatible link functions according to user-specified Bregman generators and regressor model classes, enabling flexible yet theoretically grounded debiased estimation. The framework integrates the Riesz representation theorem, cross-fitting, and diverse basis functions—including RKHS, neural embeddings, and random forest leaf encodings—to support estimation tasks such as average treatment effects (ATE), average treatment effects on the treated (ATT), and average marginal effects. It yields RA-, RW-, ARW-, and TMLE-style estimators, along with confidence intervals and p-values. The implementation is open-sourced and available on PyPI.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• causal inference
• debiased machine learning
• Riesz representation
• structural parameter estimation
• average treatment effect

Innovation

• Debiased Machine Learning
• Generalized Riesz Regression
• Automatic Regressor Balancing
• Bregman Divergence
• Causal Inference