Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel tests can detect discrepancies between interventional outcome laws, but global tests do not reveal where the laws differ. We propose DR-ME, to our knowledge the first semiparametrically efficient finite-location test for interpretable distributional treatment effects. DR-ME evaluates an interventional kernel witness at learned outcome locations, returning causal-discrepancy coordinates rather than only a global rejection. From observational data, we derive orthogonal doubly robust kernel features whose centered oracle form is the canonical gradient of this finite witness. For fixed locations, we characterize the local testing limit: DR-ME is chi-square calibrated under the null, has noncentral chi-square local power, and uses the covariance whitening that optimizes local signal-to-noise for discrepancies visible through the selected coordinates. This efficient local-power geometry yields a principled location-learning criterion, with sample splitting preserving post-selection validity. Experiments show near-nominal type-I error, competitive power against global doubly robust kernel tests, and interpretable learned locations that localize distributional effects in a semi-synthetic medical-imaging study.

翻译：分布处理效应可能对均值不可见：处理可能在保持平均结果的同时改变尾部、模态、离散性或罕见事件概率。核检验能够检测干预性结果分布之间的差异，但全局检验无法揭示分布具体在何处存在差异。我们提出DR-ME——据我们所知，这是首个针对可解释分布处理效应的半参数有效有限位置检验。DR-ME在学习到的结果位置上评估干预性核见证函数，返回因果差异坐标而非仅给出全局拒绝结果。基于观测数据，我们导出正交双重稳健核特征，其中心化神谕形式是该有限见证函数的规范梯度。对于固定位置，我们刻画了局部检验极限：DR-ME在原假设下服从卡方校准，具有非中心卡方局部功效，并采用协方差白化以优化通过选定坐标可见差异的局部信噪比。这种高效局部功效几何结构衍生出原则性的位置学习准则，通过样本分裂保持选择后有效性。实验表明，DR-ME具有接近名义水平的I型错误率、与全局双重稳健核检验相当的功效，并在半合成医学影像研究中定位出可解释的分布效应位置。