Structural intervention distance for evaluating causal graphs

TY - JOUR

T1 - Structural intervention distance for evaluating causal graphs

AU - Peters, Jonas Martin

AU - Bühlmann, Peter

PY - 2015/3/26

Y1 - 2015/3/26

N2 - 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-)metric 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 DAGs that represent Markov equivalence classes. The SID differs significantly from the widely used structural Hamming distance and therefore constitutes a valuable additional measure. We discuss properties of this distance and provide a (reasonably) efficient implementation with software code available on the first author's home page.

AB - 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-)metric 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 DAGs that represent Markov equivalence classes. The SID differs significantly from the widely used structural Hamming distance and therefore constitutes a valuable additional measure. We discuss properties of this distance and provide a (reasonably) efficient implementation with software code available on the first author's home page.

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1162/neco_a_00708

DO - 10.1162/neco_a_00708

M3 - Journal article

C2 - 25602767

SN - 0899-7667

VL - 27

SP - 771

EP - 799

JO - Neural Computation

JF - Neural Computation

IS - 3

ER -