Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Explainable artificial intelligence (XAI) aims to shed light on the opaque behavior of machine learning algorithms, which includes assessing the importance of features for a predictive algorithm. Model-agnostic post hoc methods attribute scores to input features according to their relevance for the prediction in an arbitrary, already fitted supervised machine learning model (Molnar, 2020; Murdoch et al., 2019). Refined conceptualizations include, for example, methods aiming for insights on the prediction of individual observations, like Shapley additive explanations (Lundberg and Lee, 2017), or a feature importance focus on the model's overall behavior, yielding global-level explanations. A crucial distinction in feature importance concepts is between conditional and marginal viewpoints (Strobl et al., 2008; Watson and Wright, 2021): Marginal feature importance evaluates a feature's impact irrespective of other features included in the model, whereas conditional feature importance takes the predictive information of other features into account. The presence of dependency structures, which real-world datasets frequently exhibit, plays a pivotal role in this distinction because a feature's impact on the prediction given, i.e., on top of the predictive information provided by correlated features, alters the importance score attributed (Watson and Wright, 2021).