Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment
This provides practitioners with an interpretable, model-agnostic method for model-data alignment, addressing a specific need in model validation and interpretability.
The authors tackled the problem of evaluating whether machine learning models align with the underlying data structure, proposing a simple heuristic framework that compares data-derived feature rankings with model-based explanations to assess alignment.
In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that is, whether the model says what the data says. Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline derived directly from the data itself. Drawing inspiration from Rubin's Potential Outcomes Framework, we quantify how strongly each feature separates the two outcome groups in a binary classification task, moving beyond traditional descriptive statistics to estimate each feature's effect on the outcome. By comparing these data-derived feature rankings with model-based explanations, we provide practitioners with an interpretable and model-agnostic method for assessing model-data alignment.