从因果关系出发，系统梳理决策方法与实践框架。
A Review of Causal Decision Making
- 通过因果结构、效应与策略学习，构建完整决策链条。
- 整合多类方法形成可运行的Python工具包。
- 适合研究因果推理与智能决策的学者与工程师。
为实现有效决策，需深入理解行动、环境与结果间的因果关系。本文综述了因果决策的三个核心方面：1）通过因果结构学习发现因果关系；2）通过因果效应学习理解关系影响；3）通过因果策略学习将前两者知识应用于决策支持。同时，识别了阻碍因果决策广泛应用的挑战，并讨论了近期突破进展。最后，提出未来研究方向，结合真实案例展示因果决策的应用潜力。本文旨在整合该领域各类方法，构建基于Python的完整方法论与可实践框架。项目地址：https://causaldm.github.io/Causal-Decision-Making。
原文摘要 · Abstract (English)
To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision-making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and to further enhance the implementation of causal decision-making in practice, with real-world applications illustrated based on the proposed causal decision-making. We aim to offer a comprehensive methodology and practical implementation framework by consolidating various methods in this area into a Python-based collection. URL: https://causaldm.github.io/Causal-Decision-Making.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。