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 quartets.

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，旨在突出估计因果关系时面临的挑战。这四个数据集每个都是基于不同的因果机制生成的：第一个涉及碰撞因子，第二个涉及混杂因子，第三个涉及中介变量，第四个涉及包含因素导致的M偏倚。论文对每个数据集进行了数学总结，并给出了描述变量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图形完全相同，但真实的因果效应却不同，而正确估计因果效应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地理解因果推断方法所基于的假设，并强调收集超出统计工具所能提供信息的额外信息的重要性。论文还提供了用于重现所有图形的R代码，并通过名为quartets的R包提供了数据集的访问权限。