Structural Intervention Distance (SID) for Evaluating Causal Graphs
This provides a new tool for evaluating causal graphs in causal inference, though it is incremental as it builds on existing distance measures.
The authors tackled the problem of quantifying differences between causal graphs by proposing the Structural Intervention Distance (SID), a graphical distance measure based on causal inference statements, which differs significantly from the Structural Hamming Distance (SHD).
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).