Abstract
Many empirical sciences, including the social sciences and life sciences, aim to study causal relationships. Researchers in these fields need computational methods for analyzing observed data and identifying causal structures among a set of variables. Such computational methods enable researchers to draw conclusions on the basis of both their assumptions and the observed data. Moreover, these methods are useful for developing hypotheses on causal relations, designing future observational studies, and planning future experimental studies that can potentially provide stronger evidence of estimated causal relations.
The objective of this special issue is to present an up-to-date overview of causal discovery methods, which have witnessed rapid advancements in recent years. The chief editor and guest editors invited the following three survey papers on various hot topics related to causal discovery:
