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

TL;DR

genriesz is a Python package that automates debiased machine learning for causal inference using generalized Riesz regression, enabling flexible, efficient estimation of various causal parameters with automatic balancing and diverse model options.

Contribution

The paper introduces genriesz, a novel Python package that unifies and automates debiased machine learning with generalized Riesz regression, supporting multiple estimation techniques and automatic balancing.

Findings

Supports estimation of ATE, ATT, and marginal effects.

Provides automatic regressor balancing for optimality.

Includes confidence intervals and hypothesis testing.

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.