论文标题
通过结构性因果边际问题的因果推断
Causal Inference Through the Structural Causal Marginal Problem
论文作者
论文摘要
我们基于从多个数据集的合并信息介绍了一种反事实推断的方法。我们考虑了统计边缘问题的因果重新重新制定：鉴于边际结构因果模型（SCM）的收集在不同但重叠的变量集中，请确定与边际相反一致的关节SCM集合。我们使用响应函数配方对这种分类SCM进行了形式化，并表明它降低了允许的边缘和关节SCM的空间。因此，我们的工作通过其他变量突出了一种通过其他变量的新模式，与统计数据相反。
We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: given a collection of marginal structural causal models (SCMs) over distinct but overlapping sets of variables, determine the set of joint SCMs that are counterfactually consistent with the marginal ones. We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs. Our work thus highlights a new mode of falsifiability through additional variables, in contrast to the statistical one via additional data.
