cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that purely data-driven operationalization of multivariate feature importance is unsuitable for such purposes. Even for simple problems with two features, spurious associations due to collider bias and suppression arise from considering one feature only in the observational context of the other, which can lead to misinterpretations. Causal knowledge about the data-generating process is required to identify and correct such misleading feature attributions. We propose cc-Shapley (causal context Shapley), an interventional modification of conventional observational Shapley values leveraging knowledge of the data's causal structure, thereby analyzing the relevance of a feature i
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that purely data-driven operationalization of multivariate feature importance is unsuitable for such purposes. Even for simple problems with two features, spurious associations due to collider bias and suppression arise from considering one feature only in the observational context of the other, which can lead to misinterpretations. Causal knowledge about the data-generating process is required to identify and correct such misleading feature attributions. We propose cc-Shapley (causal context Shapley), an interventional modification of conventional observational Shapley values leveraging knowledge of the data's causal structure, thereby analyzing the relevance of a feature in the causal context of the remaining features. We show theoretically that this eradicates spurious association induced by collider bias. We compare the behavior of Shapley and cc-Shapley values on various, synthetic, and real-world datasets. We observe nullification or reversal of associations compared to univariate feature importance when moving from observational to cc-Shapley.
Executive Summary
The article introduces cc-Shapley, a novel method for measuring multivariate feature importance that incorporates causal context, addressing the limitations of traditional Shapley values in observational settings. By leveraging knowledge of the data's causal structure, cc-Shapley eradicates spurious associations induced by collider bias, providing a more accurate assessment of feature importance. The authors demonstrate the effectiveness of cc-Shapley through theoretical analysis and empirical comparisons with Shapley values on synthetic and real-world datasets. The results show a nullification or reversal of associations compared to univariate feature importance when moving from observational to cc-Shapley. This work has significant implications for explainable artificial intelligence, as it enables researchers to examine and scrutinize machine learning models more effectively.
Key Points
- ▸ cc-Shapley is an interventional modification of conventional observational Shapley values that incorporates causal context.
- ▸ cc-Shapley eradicates spurious associations induced by collider bias, providing a more accurate assessment of feature importance.
- ▸ The authors demonstrate the effectiveness of cc-Shapley through theoretical analysis and empirical comparisons with Shapley values.
Merits
Strength in Addressing Collider Bias
cc-Shapley effectively eradicates spurious associations induced by collider bias, providing a more accurate assessment of feature importance.
Improved Interpretability
cc-Shapley enables researchers to examine and scrutinize machine learning models more effectively, facilitating scientific discovery.
Demerits
Limited Generalizability
The effectiveness of cc-Shapley may be limited to specific datasets and domains, requiring further validation across diverse contexts.
Dependence on Causal Knowledge
cc-Shapley requires knowledge of the data's causal structure, which may not be readily available or may be uncertain in practice.
Expert Commentary
The introduction of cc-Shapley represents a significant advancement in the field of explainable artificial intelligence. By incorporating causal context, cc-Shapley addresses a critical limitation of traditional Shapley values and provides a more accurate assessment of feature importance. However, the effectiveness of cc-Shapley may be limited to specific datasets and domains, requiring further validation across diverse contexts. Furthermore, the dependence on causal knowledge may be a challenge in practice. Nevertheless, the potential implications of cc-Shapley are substantial, enabling researchers to examine and scrutinize machine learning models more effectively and facilitating more informed decision-making.
Recommendations
- ✓ Further validation of cc-Shapley across diverse datasets and domains is necessary to fully evaluate its effectiveness.
- ✓ Developing methods for inferring causal knowledge from data is crucial to enable widespread adoption of cc-Shapley.
