Among explainability techniques, SHAP stands out as one of the most popular, but often overlooks the causal structure of the problem. In response, do-SHAP employs interventional queries, but its reliance on estimands hinders its practical application. To address this problem, we propose the use of estimand-agnostic approaches, which allow for the estimation of any identifiable query from a single model, making do-SHAP feasible on complex graphs. We also develop a novel algorithm to significantly accelerate its computation at a negligible cost, as well as a method to explain inaccessible Data Generating Processes. We demonstrate the estimation and computational performance of our approach, and validate it on two real-world datasets, highlighting its potential in obtaining reliable explanations.

翻译：在可解释性技术中，SHAP作为最流行的方法之一，却常常忽视问题的因果结构。针对这一问题，do-SHAP采用了干预性查询，但其对特定估计量的依赖阻碍了实际应用。为解决此问题，我们提出使用估计量无关的方法，该方法允许从单一模型中估计任何可识别的查询，使得do-SHAP在复杂图结构上具有可行性。我们还开发了一种新颖算法，能以可忽略的成本显著加速其计算，并提出了一种解释不可访问数据生成过程的方法。我们展示了所提方法在估计和计算方面的性能，并在两个真实世界数据集上进行了验证，突显了其在获得可靠解释方面的潜力。