We propose a constraint-based algorithm, which automatically determines
causal relevance thresholds, to infer causal networks from data. We call these
topological thresholds. We present two methods for determining the threshold:
the first seeks a set of edges that leaves no disconnected nodes in the
network; the second seeks a causal large connected component in the data.
We tested these methods both for discrete synthetic and real data, and
compared the results with those obtained for the PC algorithm, which we took as
the benchmark. We show that this novel algorithm is generally faster and more
accurate than the PC algorithm.
The algorithm for determining the thresholds requires choosing a measure of
causality. We tested our methods for Fisher Correlations, commonly used in PC
algorithm (for instance in \cite{kalisch2005}), and further proposed a discrete
and asymmetric measure of causality, that we called Net Influence, which
provided very good results when inferring causal networks from discrete data.
This metric allows for inferring directionality of the edges in the process of
applying the thresholds, speeding up the inference of causal DAGs.

使用基于约束条件的算法从数据中推断因果网络，通过确定因果关联阈值来构建拓扑结构，比传统 PC 算法更快且更准确，并引入了一种称为 Net Influence 的离散非对称因果度量来推断有向边的方向性，加速因果 DAGs 的推理过程。