Understanding how much each variable contributes to an outcome is a central question across disciplines. A causal view of explainability is favorable for its ability in uncovering underlying mechanisms and generalizing to new contexts. Based on a family of causal explainability quantities, we develop methods for their estimation and inference. In particular, we construct a one-step correction estimator using semi-parametric efficiency theory, which explicitly leverages the independence structure of variables to reduce the asymptotic variance. For a null hypothesis on the boundary, i.e., zero explainability, we show its equivalence to Fisher's sharp null, which motivates a randomization-based inference procedure. Finally, we illustrate the empirical efficacy of our approach through simulations as well as an immigration experiment dataset, where we investigate how features and their interactions shape public opinion toward admitting immigrants.

翻译：理解每个变量对结果的贡献程度是跨学科研究的核心问题。因果视角下的可解释性因其能够揭示潜在机制并推广至新情境而具有优势。基于一系列因果可解释性度量，我们开发了相应的估计与推断方法。具体而言，我们利用半参数效率理论构建了一步校正估计量，该方法显式利用变量的独立结构以降低渐近方差。针对边界上的零假设（即可解释性为零），我们证明了其与费希尔精确零假设的等价性，从而启发了基于随机化的推断程序。最后，我们通过模拟实验以及一项移民实验数据集展示了所提方法的实证效能，其中我们探究了特征及其交互作用如何影响公众对接收移民的态度。