Disentangling Interactions and Dependencies in Feature Attribution (2410.23772v1)

Abstract: In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one importance score for each feature. But often, predicting the target variable requires interactions between several features (such as in the XOR function), and features might have complex statistical dependencies that allow to partially replace one feature with another one. In commonly used feature importance scores these cooperative effects are conflated with the features' individual contributions, making them prone to misinterpretations. In this work, we derive DIP, a new mathematical decomposition of individual feature importance scores that disentangles three components: the standalone contribution and the contributions stemming from interactions and dependencies. We prove that the DIP decomposition is unique and show how it can be estimated in practice. Based on these results, we propose a new visualization of feature importance scores that clearly illustrates the different contributions.

• The paper introduces DIP, a novel method that decomposes feature importance into standalone, interaction, and dependency contributions.
• It employs a Generalized Groupwise Additive Model to isolate pure interaction effects from main effects for clearer model diagnostics.
• Evaluations on real datasets reveal that many features derive their predictive power mainly through interactions rather than standalone effects.

Disentangling Interactions and Dependencies in Feature Attribution

The paper "Disentangling Interactions and Dependencies in Feature Attribution" tackles a nuanced problem in the domain of explainable machine learning: the accurate assessment of feature importance when interactions and dependencies among features are present. This issue is vital as conventional feature attribution methods often conflate these aspects, leading to misinterpretations of feature contributions.

Key Contributions

The authors introduce a novel method named DIP (Disentangling Interactions and Dependencies) which decomposes feature importance into three distinct components: standalone contributions of the features, contributions from their interactions, and contributions from dependencies among them. The theoretical underpinning of this method is robust, backed by a proof that demonstrates its uniqueness in decomposition.

Approach and Methodology

At the core of this paper is the differentiation between a feature’s standalone effect and the cooperative effects it might exhibit in conjunction with other features. Traditional global feature importance methodologies mostly offer a single score per feature, which does not account for these intricacies. The framework introduced, DIP, addresses this shortfall by clearly visualizing the different contribution components, thereby providing a transparent estimation of feature value in a model's predictive capacity.

To implement DIP, the authors utilize an estimation procedure that involves fitting a Generalized Groupwise Additive Model (GGAM) to account for main effects while isolating pure interactions. This approach is significant because it allows the decomposition to separate pure interaction effects from main effect contributions by assessing how much information about the target can only be accessed through between-group cooperations.

Practical Implementations and Results

The authors evaluate DIP on real-world datasets, such as the wine quality and California housing datasets, where algorithmic performance metrics like Leave-One-Covariate-Out (LOCO) scores were calculated and analyzed. The findings indicate that many variables, previously thought individually important due to LOCO scores, actually derive their significance from interactions with other features. For example, longitude and latitude variables in housing data showed high importance scores primarily due to their interactions, not their standalone predictive power.

Furthermore, the authors demonstrate that many perceived standalone important features are actually due to redundant contributions related to dependencies with other features. This enhanced understanding pushed forward by DIP could lead to improved model diagnostics and better feature engineering practices by accurately delineating the role of feature interactions.

Theoretical Implications and Future Developments

The DIP methodology offers a fresh perspective that extends beyond classical interpretations of interaction effects in predictive modeling. The approach is primarily built on $L^2$ loss metrics but has potential applicability within other loss paradigms, such as cross-entropy losses widely used in binary classification tasks.

The resolution of interpreting feature importance through this lens suggests several avenues for future research. One direction is adapting this decomposition procedure for more model-specific explainer methods, which could yield an even more refined understanding of model behavior, especially in deep learning contexts where interactions are inherently more complex.

Another frontier involves tackling the computational aspects of feature attribution. While the complexity introduced by pairwise consideration is manageable as presented, the implications of extending this to larger feature sets and ensuring computational efficiency remain areas ripe for exploration. Furthermore, exploring machine learning models' fairness through this decomposition could uncover subtle biases encoded via interactions and dependencies.

Conclusion

In conclusion, the DIP framework represents a vital step forward in the interpretation of machine learning models. By disentangling interaction effects and dependencies, the authors enrich the landscape of explainable artificial intelligence, offering researchers and practitioners a powerful tool for better understanding the intricate details of feature interactions and their impact on predictive modeling. This approach not only enhances transparency but also paves the way for more reliable model diagnostics and effective policy and decision-making informed by machine learning insights.