Instrumental Variables in Causal Inference and Machine Learning: A Survey
因果推断与机器学习中的工具变量：综述AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕未观测混杂下的工具变量识别与因果效应估计展开，直接属于因果推断。
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因果推断与机器学习中的工具变量：综述
- 研究问题
- 在未充分观测混杂因素的情形下，工具变量方法如何识别因果效应，传统计量方法与机器学习研究又有哪些联系？
- 方法与思路
- 作者介绍工具变量定义和不同假设下的识别问题，将方法划分为两阶段最小二乘、控制函数和工具变量评估三条研究线。
- 材料陈述的贡献
- 摘要称该综述系统梳理经典方法及机器学习进展，汇总应用、数据集与算法，讨论开放问题，并提供相关方法工具包。
- 阅读关注点
- 应核对各类工具变量假设与识别对象的对应关系、综述覆盖范围，以及机器学习方法与经典方法的实质差异。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或期刊接收未经核实；摘要无法确认综述的完整性，工具包内容与可用性也未核验。
原文摘要
Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://github.com/causal-machine-learning-lab/mliv.