The demand for Explainable AI (XAI) has triggered an explosion of methods, producing a landscape so fragmented that we now rely on surveys of surveys. Yet, fundamental challenges persist: conflicting metrics, failed sanity checks, and unresolved debates over robustness and fairness. The only consensus on how to achieve explainability is a lack of one. This has led many to point to the absence of a ground truth for defining ``the'' correct explanation as the main culprit. This position paper posits that the persistent discord in XAI arises not from an absent ground truth but from a ground truth that exists, albeit as an elusive and challenging target: the causal model that governs the relevant system. By reframing XAI queries about data, models, or decisions as causal inquiries, we prove the necessity and sufficiency of causal models for XAI. We contend that without this causal grounding, XAI remains unmoored. Ultimately, we encourage the community to converge around advanced concept and causal discovery to escape this entrenched uncertainty.

翻译：摘要：对可解释人工智能（XAI）的需求引发了方法的爆炸性增长，导致研究景观如此碎片化，以至于我们现在依赖于综述的综述。然而，根本性挑战依然存在：冲突的评估指标、失败的合理性验证，以及关于鲁棒性和公平性的未解争议。关于如何实现可解释性的唯一共识是缺乏共识。这使许多人将缺乏定义“正确”解释的基准真相视为主要症结。本文立场论文指出，XAI中持续的分歧并非源于缺失的基准真相，而是源于一个存在但难以捉摸且极具挑战性的目标：支配相关系统的因果模型。通过将关于数据、模型或决策的XAI问题重构为因果探究，我们证明了因果模型对XAI的必要性和充分性。我们主张，缺乏这种因果基础，XAI将无所依托。最终，我们鼓励学术界围绕高级概念与因果发现形成共识，以摆脱这种根深蒂固的不确定性。