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Structural intervention distance for evaluating causal graphs.

Jonas Peters1, Peter Bühlmann

  • 1Seminar for Statistics, Department of Mathematics, ETH Zürich 8092, Switzerland peters@stat.math.ethz.ch.

Neural Computation
|January 21, 2015
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Summary
This summary is machine-generated.

We introduce the structural intervention distance (SID), a new metric to measure differences between causal graphs. This method quantifies closeness between directed acyclic graphs (DAGs) for evaluating interventions.

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Area of Science:

  • Causal inference
  • Graph theory
  • Machine learning

Background:

  • Causal inference heavily depends on graph structures like directed acyclic graphs (DAGs).
  • Variations in graph structures lead to different causal statements and intervention distributions.
  • Comparing these structures is crucial for accurate causal analysis.

Purpose of the Study:

  • To introduce a novel metric, the structural intervention distance (SID), for quantifying differences between causal graphs.
  • To provide a measure suitable for evaluating graphs used in computing interventions.
  • To compare the efficacy of SID against existing measures like the structural Hamming distance.

Main Methods:

  • Developed a new (pre-)metric, the structural intervention distance (SID), based solely on graphical criteria.
  • Applied SID to compare directed acyclic graphs (DAGs) and completed partially directed acyclic graphs (CPDAGs).
  • Implemented an efficient algorithm for calculating SID and made the software publicly available.

Main Results:

  • The SID quantifies the closeness between two DAGs based on their causal inference statements.
  • SID provides a distinct measure compared to the structural Hamming distance.
  • The proposed metric is well-suited for evaluating graphs used in intervention calculations.

Conclusions:

  • The structural intervention distance (SID) offers a valuable new approach for comparing causal graphs.
  • SID is particularly useful for assessing the impact of graph structure on intervention distributions.
  • The availability of an efficient implementation facilitates the adoption of SID in causal inference research.