This paper introduces a collection of four data sets, similar to Anscombe's Quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four data sets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The paper includes a mathematical summary of each data set, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each data set are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example data sets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The paper also includes R code for reproducing all figures and provides access to the data sets themselves through an R package named quartet.

翻译：本文介绍了一组包含四个数据集的集合，类似于安斯库姆四重奏，旨在突出估计因果效应时面临的挑战。每个数据集基于不同的因果机制生成：第一个涉及碰撞变量，第二个涉及混杂变量，第三个涉及中介变量，第四个涉及因包含因素而引发的M偏差。本文提供了每个数据集的数学概览，以及描述变量之间关系的有向无环图。尽管每个数据集的统计摘要和可视化结果相同，但其真实因果效应却不同，正确估计需要了解数据生成机制。这些示例数据集有助于从业者更深入理解因果推断方法所依赖的假设，并强调仅凭统计工具无法获取足够信息，必须收集更多信息的重要性。本文还提供了用于复现所有图形的R代码，并通过名为quartet的R包提供数据集访问。