  Causal analysis has become an essential component in understanding the
underlying causes of phenomena across various fields. Despite its significance,
the existing literature on causal discovery algorithms is fragmented, with
inconsistent methodologies and a lack of comprehensive evaluations. This study
addresses these gaps by conducting an exhaustive review and empirical
evaluation of causal discovery methods for numerical data, aiming to provide a
clearer and more structured understanding of the field. Our research began with
a comprehensive literature review spanning over a decade, revealing that
existing surveys fall short in covering the vast array of causal discovery
advancements. We meticulously analyzed over 200 scholarly articles to identify
24 distinct algorithms. This extensive analysis led to the development of a
novel taxonomy tailored to the complexities of causal discovery, categorizing
methods into six main types. Addressing the lack of comprehensive evaluations,
our study conducts an extensive empirical assessment of more than 20 causal
discovery algorithms on synthetic and real-world datasets. We categorize
synthetic datasets based on size, linearity, and noise distribution, employing
5 evaluation metrics, and summarized the top-3 algorithm recommendations for
different data scenarios. The recommendations have been validated on 2
real-world datasets. Our results highlight the significant impact of dataset
characteristics on algorithm performance. Moreover, a metadata extraction
strategy was developed to assist users in algorithm selection on unknown
datasets. The accuracy of estimating metadata is higher than 80%. Based on
these insights, we offer professional and practical recommendations to help
users choose the most suitable causal discovery methods for their specific
dataset needs.
