Doubly robust learning offers a robust framework for causal inference from
observational data by integrating propensity score and outcome modeling.
Despite its theoretical appeal, practical adoption remains limited due to
perceived complexity and inaccessible software. This tutorial aims to demystify
doubly robust methods and demonstrate their application using the EconML
package. We provide an introduction to causal inference, discuss the principles
of outcome modeling and propensity scores, and illustrate the doubly robust
approach through simulated case studies. By simplifying the methodology and
offering practical coding examples, we intend to make doubly robust learning
accessible to researchers and practitioners in data science and statistics.

该论文介绍了双重稳健学习方法，旨在通过整合倾向得分和结果建模来从观测数据中进行因果推断。通过使用 EconML 软件包演示其应用，该论文消除了双重稳健方法的复杂性，并提供了实际的编程示例，旨在使数据科学和统计学的研究人员和从业人员可以更便捷地应用双重稳健学习方法。

因果推断的双重稳健学习教程

A Tutorial on Doubly Robust Learning for Causal Inference

In recommendation systems (RS), user behavior data is observational rather
than experimental, resulting in widespread bias in the data. Consequently,
tackling bias has emerged as a major challenge in the field of recommendation
systems. Recently, Doubly Robust Learning (DR) has gained significant attention
due to its remarkable performance and robust properties. However, our
experimental findings indicate that existing DR methods are severely impacted
by the presence of so-called Poisonous Imputation, where the imputation
significantly deviates from the truth and becomes counterproductive.
To address this issue, this work proposes Conservative Doubly Robust strategy
(CDR) which filters imputations by scrutinizing their mean and variance.
Theoretical analyses show that CDR offers reduced variance and improved tail
bounds.In addition, our experimental investigations illustrate that CDR
significantly enhances performance and can indeed reduce the frequency of
poisonous imputation.

为解决推荐系统中的数据偏差问题，本研究提出一种名为保守双重稳健策略（CDR）的方法，通过检验均值和方差来过滤造成负面影响的代入值。理论分析表明，CDR 具有降低方差和改善尾部边界的优势，并通过实证研究证明，CDR 显著提高了性能并减少了有害的代入值的频率。

CDR: 保守双重稳健学习以消除偏向的推荐

CDR: Conservative Doubly Robust Learning for Debiased Recommendation

In recommender systems, a common problem is the presence of various biases in
the collected data, which deteriorates the generalization ability of the
recommendation models and leads to inaccurate predictions. Doubly robust (DR)
learning has been studied in many tasks in RS, with the advantage that unbiased
learning can be achieved when either a single imputation or a single propensity
model is accurate. In this paper, we propose a multiple robust (MR) estimator
that can take the advantage of multiple candidate imputation and propensity
models to achieve unbiasedness. Specifically, the MR estimator is unbiased when
any of the imputation or propensity models, or a linear combination of these
models is accurate. Theoretical analysis shows that the proposed MR is an
enhanced version of DR when only having a single imputation and propensity
model, and has a smaller bias. Inspired by the generalization error bound of
MR, we further propose a novel multiple robust learning approach with
stabilization. We conduct extensive experiments on real-world and
semi-synthetic datasets, which demonstrates the superiority of the proposed
approach over state-of-the-art methods.

本文提出了一种多重稳健估计器，该估计器可以利用多个候选值进行无偏估计，并介绍了一种新的具有稳定性的多重稳健学习方法，实验结果表明，该方法具有优越的性能.