  The interpretability of prediction mechanisms with respect to the underlying
prediction problem is often unclear. While several studies have focused on
developing prediction models with meaningful parameters, the causal
relationships between the predictors and the actual prediction have not been
considered. Here, we connect the underlying causal structure of a data
generation process and the causal structure of a prediction mechanism. To
achieve this, we propose a framework that identifies the feature with the
greatest causal influence on the prediction and estimates the necessary causal
intervention of a feature such that a desired prediction is obtained. The
general concept of the framework has no restrictions regarding data linearity;
however, we focus on an implementation for linear data here. The framework
applicability is evaluated using artificial data and demonstrated using
real-world data.
