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.

翻译：因果推断在医疗健康、市场营销、政治学及在线广告等多个领域具有广泛的实际应用。作为因果推断中的基础问题，处理效应估计已在统计学领域被深入研究数十年。然而，传统处理方法估计方法难以有效处理大规模高维异质数据。近年来，一个新兴研究方向在广义人工智能领域受到日益关注，该方向融合了传统处理效应估计方法（如倾向性评分、匹配与重加权）与先进机器学习方法（如表征学习、对抗学习及图神经网络）的优势。尽管先进机器学习方法在处理效应估计中展现出卓越性能，但也带来了众多新课题与研究问题。基于因果推断领域的最新研究成果，我们围绕处理效应估计任务的三大核心要素——处理变量、协变量与结果变量——进行了全面的挑战与机遇讨论。此外，我们从多角度展示了该主题具有前景的研究方向。