CXPlain: Causal Explanations for Model Interpretation under Uncertainty (1910.12336v1)

Abstract: Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges. Here, we frame the task of providing explanations for the decisions of machine-learning models as a causal learning task, and train causal explanation (CXPlain) models that learn to estimate to what degree certain inputs cause outputs in another machine-learning model. CXPlain can, once trained, be used to explain the target model in little time, and enables the quantification of the uncertainty associated with its feature importance estimates via bootstrap ensembling. We present experiments that demonstrate that CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance. In addition, we confirm that the uncertainty estimates provided by CXPlain ensembles are strongly correlated with their ability to accurately estimate feature importance on held-out data.

• The paper introduces CXPlain, a novel method applying causal explanation models to estimate feature importance without modifying existing models.
• It uses bootstrap resampling to quantify uncertainty in feature importance, enhancing the reliability of model interpretations.
• Experimental results demonstrate that CXPlain outperforms state-of-the-art techniques in accuracy and computational efficiency on benchmarks like MNIST and ImageNet.

Overview of "CXPlain: Causal Explanations for Model Interpretation under Uncertainty"

The paper "CXPlain: Causal Explanations for Model Interpretation under Uncertainty," authored by Patrick Schwab and Walter Karlen, provides significant insights into the domain of feature importance estimation in machine learning models. This work addresses pivotal challenges associated with providing not only accurate and rapid estimates of feature importance but also with quantifying the uncertainty surrounding such estimates.

The authors frame the problem of explaining machine learning model decisions through a causal learning lens, proposing the CXPlain methodology as a solution. CXPlain trains causal explanation models designed to ascertain the causal impact of inputs on the outputs of another concurrent machine-learning model. These causal explanation models are applicable across any typical machine learning models without necessitating their retraining or modification. The paper’s experimental results demonstrate that CXPlain outperforms existing model-agnostic feature importance estimation techniques in terms of accuracy and computational efficiency. Additionally, uncertainty estimates derived from CXPlain demonstrate strong correlation with its effectiveness in estimating feature importance on unseen data.

Contributions and Methodology

The authors make the following key contributions:

1. Introduction of CXPlain Models: They present a novel approach named causal explanation (CXPlain) models which focus on learning to estimate feature importance accurately for any machine learning model.
2. Bootstrap Resampling for Uncertainty Quantification: The paper outlines a method leveraging bootstrap resampling to quantify the uncertainty in feature importance estimates provided by CXPlain, enhancing the interpretability of the results.
3. Experimental Validation: CXPlain is empirically validated against existing state-of-the-art methods, showcasing superior performance both in accuracy and computational time required for evaluation.

The CXPlain methodology treats the task of feature importance estimation as a causal problem, employing a causal objective function to train supervised models. This allows for effective learning of feature importance without retraining the target predictive model. CXPlain models apply to any machine-learning model, regardless of underlying algorithms, and are characterized by employing pre-computed error contributions of input features for the formulation of causal objectives.

Experiments and Numerical Results

The experiments conducted in the paper focus on both MNIST and ImageNet benchmarks for image classification, comparing CXPlain against contemporary techniques such as LIME, SHAP, DeepSHAP, and gradient-based methods. Notably, CXPlain using a U-net architecture showed superior performance on deterministic tasks, effectively identifying important image regions with higher accuracy than competing methods.

Quantitatively, CXPlain distinguished itself by its ability to offer significant computational efficiency, proving faster than LIME and SHAP, especially evident in high-dimensional inputs like ImageNet images, where CXPlain achieved noteworthy speed advantages.

Uncertainty Estimation

The paper underscores the importance of reliability in explanation methods by addressing uncertainty quantification. Bootstrap ensembles are utilized for this purpose, producing uncertainty estimates that significantly correlate with explanation accuracy on new datasets, a feature not prominently covered by existing feature importance estimation methods.

Implications and Future Work

CXPlain’s approach to feature importance and uncertainty estimation pushes forward methods suitable for complex and diverse machine-learning models. The implications for future AI developments are evident, suggesting pathways for more transparent and reliable model interpretations. Furthermore, the authors suggest the exploration of other explanation model classes beyond neural networks, possibly enhancing the efficacy of CXPlain across varied applications.

Conclusion

The CXPlain model represents a notable advancement in the field of model interpretability under uncertainty. It is particularly relevant for applications requiring robust and efficient explanations of predictive models' decisions. The scalability and applicability of the CXPlain methodology across diverse machine learning contexts present a versatile tool that is crucial for both academic and practical advancements in AI interpretability. The authors anticipate further enhancement of the CXPlain models, possibly integrating more complex model architectures tailored to specific data modalities and application domains.