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Causal inference has numerous real-world applications in many domains, suchas health care, marketing, political science, and online advertising. Treatmenteffect estimation, a fundamental problem in causal inference, has beenextensively studied in statistics for decades. However, traditional treatmenteffect estimation methods may not well handle large-scale and high-dimensionalheterogeneous data. In recent years, an emerging research direction hasattracted increasing attention in the broad artificial intelligence field,which combines the advantages of traditional treatment effect estimationapproaches (e.g., propensity score, matching, and reweighing) and advancedmachine learning approaches (e.g., representation learning, adversariallearning, and graph neural networks). Although the advanced machine learningapproaches have shown extraordinary performance in treatment effect estimation,it also comes with a lot of new topics and new research questions. In view ofthe latest research efforts in the causal inference field, we provide acomprehensive discussion of challenges and opportunities for the three corecomponents of the treatment effect estimation task, i.e., treatment,covariates, and outcome. In addition, we showcase the promising researchdirections of this topic from multiple perspectives.

Causal Effect Estimation: Recent Advances, Challenges, and Opportunities