Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
因果效应估计：近期进展、挑战与机会AI 解读 ↓
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因果效应估计：近期进展、挑战与机会
- 研究问题
- 面对大规模、高维和异质数据，传统治疗效应估计与机器学习如何结合，治疗、协变量和结果三个环节分别面临哪些挑战？
- 方法与思路
- 作者综述倾向得分、匹配及重加权与表征学习、对抗学习和图神经网络的结合，并按治疗、协变量及结果组织讨论。
- 材料陈述的贡献
- 摘要称，文章综合梳理治疗效应估计的挑战与机会，并从多个角度提出未来研究方向；摘要未陈述新的识别定理或估计量。
- 阅读关注点
- 应核对综述覆盖范围、文献选择标准和各方法的识别前提，区分因果识别、估计误差改善与预测表现之间的论证。
- 阅读边界
- 依据的是 arXiv 预印本摘要，不能确认同行评审或期刊接收；摘要未提供系统检索标准，关于机器学习表现的概括需逐项核对证据。
原文摘要
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising. Treatment effect estimation, a fundamental problem in causal inference, has been extensively studied in statistics for decades. However, traditional treatment effect estimation methods may not well handle large-scale and high-dimensional heterogeneous data. In recent years, an emerging research direction has attracted increasing attention in the broad artificial intelligence field, which combines the advantages of traditional treatment effect estimation approaches (e.g., propensity score, matching, and reweighing) and advanced machine learning approaches (e.g., representation learning, adversarial learning, and graph neural networks). Although the advanced machine learning approaches have shown extraordinary performance in treatment effect estimation, it also comes with a lot of new topics and new research questions. In view of the latest research efforts in the causal inference field, we provide a comprehensive discussion of challenges and opportunities for the three core components of the treatment effect estimation task, i.e., treatment, covariates, and outcome. In addition, we showcase the promising research directions of this topic from multiple perspectives.