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

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 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…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Bayesian Modeling and Causal Inference
