  In social sciences and economics, causal inference traditionally focuses on
assessing the impact of predefined treatments (or interventions) on predefined
outcomes, such as the effect of education programs on earnings. Causal
discovery, in contrast, aims to uncover causal relationships among multiple
variables in a data-driven manner, by investigating statistical associations
rather than relying on predefined causal structures. This approach, more common
in computer science, seeks to understand causality in an entire system of
variables, which can be visualized by causal graphs. This survey provides an
introduction to key concepts, algorithms, and applications of causal discovery
from the perspectives of economics and social sciences. It covers fundamental
concepts like d-separation, causal faithfulness, and Markov equivalence,
sketches various algorithms for causal discovery, and discusses the back-door
and front-door criteria for identifying causal effects. The survey concludes
with more specific examples of causal discovery, e.g. for learning all
variables that directly affect an outcome of interest and/or testing
identification of causal effects in observational data.
