  Feature selection is a crucial preprocessing step in data analytics and
machine learning. Classical feature selection algorithms select features based
on the correlations between predictive features and the class variable and do
not attempt to capture causal relationships between them. It has been shown
that the knowledge about the causal relationships between features and the
class variable has potential benefits for building interpretable and robust
prediction models, since causal relationships imply the underlying mechanism of
a system. Consequently, causality-based feature selection has gradually
attracted greater attentions and many algorithms have been proposed. In this
paper, we present a comprehensive review of recent advances in causality-based
feature selection. To facilitate the development of new algorithms in the
research area and make it easy for the comparisons between new methods and
existing ones, we develop the first open-source package, called CausalFS, which
consists of most of the representative causality-based feature selection
algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we
conduct extensive experiments to compare the representative algorithms with
both synthetic and real-world data sets. Finally, we discuss some challenging
problems to be tackled in future causality-based feature selection research.
