Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the criterion of embarrassingly causal scenarios, where the existence of an exposure outcome relationship is so uncontroversial that the assumptions needed to include the corresponding causal edge in a DAG can be reasonably made. Using the case of Magic The Gathering booster draft decisions and gameplay outcomes, we show how purely observational data from 17Lands are widely and effectively used to guide draft choices despite substantial confounding, selection effects, and post treatment conditioning. We argue that the embarrassingly causal quality is a sufficient condition for justifying the construction of causal estimands and the collection of observational data to estimate them. Correspondingly, we provide guidance on evaluating observational causal inference assumptions for authors, reviewers, and readers.

翻译：观察数据常被用于回答因果问题，但此举的合法性通常被认为取决于强有力且受领域支持的关于潜在因果结构的假设，然而关于需要多少领域知识支持才能证明将有向无环图中感兴趣的因果边包含进来是合理的，目前指导有限。我们引入了"令人尴尬的因果场景"这一标准，在此类场景中，暴露-结局关系的存在如此无争议，以至于在DAG中包含相应因果边所需的假设可合理成立。以《魔法风云会》补充包轮抽决策与对局结果为例，我们展示了来自17Lands的纯观察数据如何在存在显著混淆、选择效应及处理后条件作用的情况下，被广泛且有效地用于指导轮抽选择。我们认为"令人尴尬的因果"属性足以证明构建因果估计量并收集观察数据以估计其值的合理性。相应地，我们为作者、审稿人和读者提供了评估观察性因果推断假设的指导。