论文标题
使用直观的图形界面对因果效应的紧密符号界限的可访问计算
Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface
论文作者
论文摘要
在因果点估计中，强烈的不可测试的假设几乎是普遍的。在特定设置中，可以得出边界以缩小因果效应的可能范围。符号界限适用于所有可以使用相同的定向无环图（DAG）描绘的所有设置，并且具有相同的感兴趣效果。尽管以前已经开发出了得出符号界限的方法的核心，但缺乏实施和计算的手段。我们的R包Causaloptim旨在通过实施Sachs等人的方法来解决此可用性问题。 （2022a）并通过闪亮为用户提供图形接口，该界面允许输入以大多数对因果推理感兴趣的研究人员熟悉的方式； DAG（通过点击经验），并使用熟悉的反事实表示法指定了感兴趣的因果关系。
Strong untestable assumptions are almost universal in causal point estimation. In particular settings, bounds can be derived to narrow the possible range of a causal effect. Symbolic bounds apply to all settings that can be depicted using the same directed acyclic graph (DAG) and for the same effect of interest. Although the core of the methodology for deriving symbolic bounds has been previously developed, the means of implementation and computation have been lacking. Our R-package causaloptim aims to solve this usability problem by implementing the method of Sachs et al. (2022a) and providing the user with a graphical interface through shiny that allows for input in a way that most researchers with an interest in causal inference will be familiar; a DAG (via a point-and-click experience) and specifying a causal effect of interest using familiar counterfactual notation.
