Sufficient and Necessary Explanations (and What Lies in Between)
<|reference_start|>Sufficient and Necessary Explanations (and What Lies in Between): As complex machine learning models continue to find applications in high-stakes decision-making scenarios, it is crucial that we can explain and understand their predictions. Post-hoc explanation methods provide useful insights by identifying important features in an input $\mathbf{x}$ with respect to the model output $f(\mathbf{x})$. In this work, we formalize and study two precise notions of feature importance for general machine learning models: sufficiency and necessity. We demonstrate how these two types of explanations, albeit intuitive and simple, can fall short in providing a complete picture of which features a model finds important. To this end, we propose a unified notion of importance that circumvents these limitations by exploring a continuum along a necessity-sufficiency axis. Our unified notion, we show, has strong ties to other popular definitions of feature importance, like those based on conditional independence and game-theoretic quantities like Shapley values. Crucially, we demonstrate how a unified perspective allows us to detect important features that could be missed by either of the previous approaches alone.<|reference_end|>
@article{bharti2024sufficient,
    title={Sufficient and Necessary Explanations (and What Lies in Between)},
    author={Beepul Bharti, Paul Yi, Jeremias Sulam},
    journal={arXiv preprint arXiv:2409.20427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20427},
    primaryClass={stat.ML cs.AI cs.LG}
}