Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation (2003.03934v3)

Abstract: Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. Moreover, to generate more human-friendly explanations, recent work on interpretability tries to answer questions related to causality such as "Why does this model makes such decisions?" or "Was it a specific feature that caused the decision made by the model?". In this work, models that aim to answer causal questions are referred to as causal interpretable models. The existing surveys have covered concepts and methodologies of traditional interpretability. In this work, we present a comprehensive survey on causal interpretable models from the aspects of the problems and methods. In addition, this survey provides in-depth insights into the existing evaluation metrics for measuring interpretability, which can help practitioners understand for what scenarios each evaluation metric is suitable.

Overview of Causal Interpretability in Machine Learning

The paper "Causal Interpretability for Machine Learning - Problems, Methods and Evaluation," authored by Raha Moraffah et al., tackles the critical issue of interpretability in ML models with a focus on causal frameworks. As ML models increasingly impact domains such as healthcare, law, and autonomous vehicles, understanding the decision-making processes of these models becomes vital. The authors present a comprehensive survey of causal interpretability models, highlighting the challenges, methodologies, and avenues for evaluation, which address the limitations of traditional interpretability methods.

Key Contributions

1. Contextual Background: The paper begins with an exposition of the opaque nature of ML models, often described as "black boxes," and reiterates the need for interpretable models to ensure reliability, trustworthiness, and compliance with ethical considerations such as fairness and bias mitigation.
2. Traditional vs. Causal Interpretability: Traditional interpretability approaches are categorized into inherent interpretability and post-hoc methods. Although these approaches have contributed significantly, they are limited to correlational explanations. The paper elaborates on causal interpretability, transcending traditional methods to address "why" and "what-if" scenarios through causal inference, thus supporting decision-making under hypothetical conditions.
3. Causal Models and Methods:
   • Model-Based Interpretations: Methods that estimate the causal impact of model components on outcomes, thereby explaining the role and significance of each component.
   • Counterfactual Explanations: Generating counterfactual examples to comprehend model decisions under alternate scenarios. This addresses hypothetical queries and aids in identifying feature importance in decision outcomes.
   • Fairness in Machine Learning: The paper addresses fairness using causal methods, arguing that interpretable models are essential for ensuring ethical standards in ML applications. The authors explore frameworks that utilize causal reasoning to ensure fair decision-making.
   • Verification of Causal Relationships: Causal interpretability also serves as a foundation for verifying causal relationships in data, thus ensuring the robustness and accuracy of ML models.
4. Evaluation Methods: Evaluating causal interpretability remains an open challenge. The paper provides in-depth insight into potential evaluation metrics, including human subject-based assessments and non-human-based metrics, ensuring that interpretability aligns with human intuition and provides faithful model explanation.
5. Ethical and Practical Implications: The practical implications of causal interpretability lie in its potential to enhance model reliability, ensure fairness, and align ML outcomes with regulations such as the European Union's "Right to Explanation." Theoretically, it refines the understanding of causal relationships in ML, which is crucial for model generalization and adaptability.

Implications and Future Directions

The exploration of causal interpretability frameworks indicates a significant advancement over traditional interpretability methods by addressing the causal nature of decisions in ML models. This transition supports the development of systems that are not only interpretable but also ethically aligned. The survey highlights the necessity for further research in developing robust causal models and standardized evaluation metrics that can accurately measure the intended causal effects and interpretability of ML models.

The future of AI development, as inferred from this work, will likely incorporate more sophisticated causal reasoning frameworks to address interpretability challenges. By prioritizing explainability, models can gain wider acceptance and trust from users across critical applications, making ML advancements more aligned with human-centered approaches.