  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.
