A hierarchical decomposition for explaining ML performance discrepancies
Jean Feng Harvineet Singh Fan Xia Adarsh Subbaswamy Alexej Gossmann
Machine learning (ML) algorithms can often differ in performance across
domains. Understanding $\textit{why}$ their performance differs is crucial for
determining what types of interventions (e.g., algorithmic or operational) are
most effective at closing the performance gaps. Existing methods focus on
$\textit{aggregate decompositions}$ of the total performance gap into the
impact of a shift in the distribution of features $p(X)$ versus the impact of a
shift in the conditional distribution of the outcome $p(Y|X)$; however, such
coarse explanations offer only a few options for how one can close the
performance gap. $\textit{Detailed variable-level decompositions}$ that
quantify the importance of each variable to each term in the aggregate
decomposition can provide a much deeper understanding and suggest much more
targeted interventions. However, existing methods assume knowledge of the full
causal graph or make strong parametric assumptions. We introduce a
nonparametric hierarchical framework that provides both aggregate and detailed
decompositions for explaining why the performance of an ML algorithm differs
across domains, without requiring causal knowledge. We derive debiased,
computationally-efficient estimators, and statistical inference procedures for
asymptotically valid confidence intervals.