attributionbyattribute

attributionbyattribute is a framework used in data analysis and interpretable machine learning to attribute the effect of a target variable to individual input attributes or features. The goal is to produce an additive decomposition of a prediction or outcome so that the contribution of each attribute can be examined, compared, and audited. The approach can be applied to single predictions as well as across populations, enabling both case-based explanations and global insights.

Methods commonly employed in attributionbyattribute include additive feature attribution approaches that assign a numeric contribution to each attribute. Shapley-value based attribution (SHAP) distributes the predicted value among attributes in a way that preserves additivity and accounts for interactions. Gradient-based attribution methods such as Integrated Gradients apply to differentiable models. Regression-based attribution uses partial effects from a fitted model to allocate influence to attributes, while marginal attribution examines changes in the outcome when varying an attribute while holding others constant. ANOVA-style decompositions also provide a variance-based view of attribute contributions.

Applications of attributionbyattribute span explainable AI, model auditing, risk assessment, marketing analytics, and policy evaluation. By clarifying which attributes drive a prediction or outcome, it supports transparency, debugging, and informed decision-making.

Limitations include sensitivity to model choice and data distribution, especially when attributes interact strongly or are highly correlated. Attribution can be ambiguous in the presence of complex interactions, and causal interpretation requires careful experimental or observational design. The term is used variably and overlaps with broader concepts of feature attribution in the field.