ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
It addresses the need for efficient and generalizable post-hoc explanations for complex models, with potential applications in domains like healthcare, though it is incremental as it builds on existing counterfactual methods.
The paper tackles the problem of generating counterfactual explanations for machine learning models by introducing ReLAX, a model-agnostic algorithm that uses deep reinforcement learning to produce optimal counterfactuals, resulting in sparser explanations and better scalability compared to existing baselines.
Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task and then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. Extensive experiments conducted on several tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, to demonstrate the usefulness of our method in a real-world use case, we leverage CFs generated by ReLAX to suggest actions that a country should take to reduce the risk of mortality due to COVID-19. Interestingly enough, the actions recommended by our method correspond to the strategies that many countries have actually implemented to counter the COVID-19 pandemic.