Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. Under assumptions about the data-generative process, the causal graph can often be identified up to an equivalence class. Proposing new realistic assumptions to circumscribe such equivalence classes is an active field of research. In this work, we propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables. We thus introduce typed directed acyclic graphs, in which variable types are used to determine the validity of causal relationships. We demonstrate, both theoretically and empirically, that the proposed assumptions can result in significant gains in the identification of the causal graph.
翻译:从观测数据中得出的因果发现是一项具有挑战性的任务,总不能找到确切的解决办法。根据对数据生成过程的假设,因果图表往往可以确定到等值等级。提出新的现实假设以限定此类等值类别是一个积极的研究领域。在这项工作中,我们提出一套新的假设,根据变量的性质限制可能的因果关系。我们因此采用打字定向循环图,其中使用可变类型来确定因果关系的有效性。我们从理论上和从经验上证明,拟议的假设可以在确定因果图表方面取得重大收益。