Abstract: Sociologists have long focused on methods for causal identification and their applications, but have paid relatively little attention to advances in causal discovery. Building on a review of the two theoretical traditions of causal inference, this paper provides a systematic introduction to classical causal discovery algorithms, examining their underlying principles, practical applications, and potential integration with other computational approaches. In an era marked by the rise of large language models, the adoption of causal discovery algorithms offers new disciplinary capabilities for computational sociology, fostering data-driven knowledge production. These algorithms provide a rich toolkit for gaining deeper insights into complex social phenomena and for exploring causal relationships. Moreover, they hold profound implications for advancing a paradigm shift in knowledge production within the humanities and social sciences in the age of digital intelligence.