🤖 AI Summary
Existing interpretability methods lack standardized data benchmarks and struggle to assess whether model explanations genuinely reflect the intrinsic structure of training data.
Method: We propose a model-agnostic framework for evaluating model-data consistency, grounded in Rubin’s potential outcomes framework to construct a model-free, data-driven baseline. This baseline quantifies the true separative effect of each feature on binary classification tasks. Model explanations are then diagnosed by comparing feature importance rankings against this causal, data-derived baseline.
Contribution/Results: Our approach efficiently detects when models deviate from fundamental data-generating mechanisms. It offers strong interpretability, low computational overhead, and cross-model applicability. To our knowledge, it is the first causally grounded, feature-effect-based tool for validating model-data consistency—providing a foundational method for trustworthy AI evaluation.
📝 Abstract
In this work, we propose a simple and computationally efficient framework to evaluate whether machine learning models align with the structure of the data they learn from; that is, whether extit{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 against model-based explanations, we provide practitioners with an interpretable and model-agnostic method to assess model--data alignment.