Causal inference relies on the structure of a graph, often a directed acyclic
graph (DAG). Different graphs may result in different causal inference
statements and different intervention distributions. To quantify such
differences, we propose a (pre-) distance between DAGs, the structural
intervention distance (SID). The SID is based on a graphical criterion only and
quantifies the closeness between two DAGs in terms of their corresponding
causal inference statements. It is therefore well-suited for evaluating graphs
that are used for computing interventions. Instead of DAGs it is also possible
to compare CPDAGs, completed partially directed acyclic graphs that represent
Markov equivalence classes. Since it differs significantly from the popular
Structural Hamming Distance (SHD), the SID constitutes a valuable additional
measure. We discuss properties of this distance and provide an efficient
implementation with software code available on the first author's homepage (an
R package is under construction).

本文提出了结构干预距离（SID）作为有向无环图（DAG）之间差别的一种度量方法，并与流行的结构汉明距离（SHD）相比较。该度量方法适用于计算干预的图形，并且可以比较部分完成的有向无环图（CPDAGs）的马尔可夫等价类。