Estimation of interventional effects of features on prediction
This work addresses interpretability issues in prediction models for researchers and practitioners, but it is incremental as it builds on existing causal methods.
The authors tackled the problem of unclear interpretability in prediction mechanisms by connecting causal structures of data generation and prediction, proposing a framework to identify the most causally influential feature and estimate interventions for desired predictions, with evaluation on artificial and real-world data.
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.