Beyond Shapley: Efficient Computation of Asymmetric Shapley Values

Researchers present novel algorithms for computing Asymmetric Shapley Values (ASV), a machine learning explainability method that integrates causal knowledge. The work demonstrates polynomial-time computation in contexts where standard SHAP is #P-hard, with specialized algorithms for tree-structured causal graphs and approximation techniques for general directed acyclic graphs.

This research addresses a fundamental computational challenge in explainable AI by advancing methods for calculating feature attribution in machine learning models. Asymmetric Shapley Values represent an evolution beyond standard Shapley value approaches by incorporating causal relationships, enabling more accurate explanations of model behavior when causal information is available. The authors establish that ASV computation can achieve polynomial time complexity in specific scenarios where existing SHAP methods face exponential computational barriers, representing a meaningful theoretical breakthrough.

The work builds on growing recognition that explainability is essential for deploying ML models in high-stakes domains such as finance, healthcare, and autonomous systems. Traditional Shapley value computation suffers from computational intractability in many practical settings, limiting real-world applicability. By introducing equivalence classes over topological orderings and demonstrating polynomial-time solutions for tree-structured causal graphs, the researchers provide implementable alternatives for common real-world scenarios.

For practitioners deploying machine learning systems—particularly in regulated industries requiring model transparency—these advances enable more efficient feature attribution analysis. The approximation algorithms for arbitrary DAGs extend applicability beyond simplified causal structures. Development teams can now perform explainability analysis on complex models without prohibitive computational costs, improving model governance and regulatory compliance efforts.

Future work likely focuses on empirical validation across diverse causal structures and integration with production ML pipelines. The sampling mechanism for topological ordering and its variants warrant further optimization as implementations scale. Broader adoption depends on software libraries incorporating these algorithms and demonstrating performance advantages over existing approximation methods in industrial applications.

• →ASV computation achieves polynomial time complexity where standard SHAP remains #P-hard, solving a major computational bottleneck
• →Equivalence classes over topological orderings enable significant computational efficiency improvements for tree-structured causal graphs
• →Approximation algorithms for arbitrary DAGs extend the method's applicability to realistic causal structures
• →Enhanced explainability efficiency supports better model governance in regulated industries requiring transparency
• →Practical viability demonstrated experimentally, enabling real-world deployment of causally-informed feature attribution