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：本文介绍了一组与安斯库姆四重图类似的数据集集合，旨在阐明估算因果效应时面临的挑战。这四组数据集分别基于不同的因果机制生成：第一组涉及碰撞变量，第二组涉及混杂变量，第三组涉及中介变量，第四组涉及引入因子导致的M偏倚。本文对每组数据集进行了数学总结，并绘制了描述变量间关系的有向无环图。尽管各组数据集的统计摘要与可视化结果完全相同，但其真实因果效应却存在差异，而正确估算该效应需要了解数据生成机制。这些示例数据集有助于实践者更深入理解因果推断方法所依据的假设，并强调仅依靠统计工具获取信息之外的额外数据收集的重要性。本文还提供了用于复现所有图形的R语言代码，并通过名为quartets的R语言包提供数据集的访问权限。