genriesz：一个用于自动去偏机器学习的Python包，基于广义里斯茨回归
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
AI总结：
genriesz是一个基于广义里斯茨回归的Python包，通过自动去偏机器学习实现因果参数的高效估计。
摘要：
利用里斯茨表示定理和去偏机器学习（DML）可以高效地估计因果和结构参数。我们介绍了genriesz，一个开源的Python包，实现了自动DML和广义里斯茨回归，这是一个统一的框架，用于通过最小化经验Bregman发散性来估计里斯茨表示器。该框架包括协变量平衡、最近邻匹配、校准估计和密度比估计作为特殊情况。该包的关键设计原则是自动回归平衡（ARB）：给定一个Bregman生成器g和一个表示器模型类，genriesz会自动构造一个兼容的链接函数，使得广义里斯茨回归估计器在用户选择的基中满足平衡（矩匹配）最优条件。该包提供了一个模块化的接口，用于指定（i）通过黑盒评估Oracle指定目标线性泛函，（ii）通过基函数（多项式、RKHS近似、随机森林叶编码、神经嵌入和最近邻捕集基）指定表示器模型，以及（iii）Bregman生成器，可选地提供用户指定的导数。它返回回归调整（RA）、里斯茨加权（RW）、增强里斯茨加权（ARW）以及TMLE风格的估计器，具有交叉验证、置信区间和p值。我们强调了估计问题如平均治疗效应（ATE）、受治疗的平均治疗效应（ATT）和平均边际效应估计的代表性工作流程。该Python包可在https://github.com/MasaKat0/genriesz和PyPI上获得。
英文摘要：
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.
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