  Causal learning has garnered significant attention in recent years because it
reveals the essential relationships that underpin phenomena and delineates the
mechanisms by which the world evolves. Nevertheless, traditional causal
learning methods face numerous challenges and limitations, including
high-dimensional, unstructured variables, combinatorial optimization problems,
unobserved confounders, selection biases, and estimation inaccuracies. Deep
causal learning, which leverages deep neural networks, offers innovative
insights and solutions for addressing these challenges. Although numerous deep
learning-based methods for causal discovery and inference have been proposed,
there remains a dearth of reviews examining the underlying mechanisms by which
deep learning can enhance causal learning. In this article, we comprehensively
review how deep learning can contribute to causal learning by tackling
traditional challenges across three key dimensions: representation, discovery,
and inference. We emphasize that deep causal learning is pivotal for advancing
the theoretical frontiers and broadening the practical applications of causal
science. We conclude by summarizing open issues and outlining potential
directions for future research.
