论文标题
用任意介入分布作为输入的识别方法
Identification Methods With Arbitrary Interventional Distributions as Inputs
论文作者
论文摘要
因果推理通过估计数据反事实参数来量化因果关系。这需要使用\ emph {识别理论}来建立感兴趣的反事实参数与可用数据的分布之间的联系。从\ emph {观察到的数据分布}来表征了多种因果参数的非参数识别的工作线。最近，识别结果已扩展到可以提供介入分布的实验数据的设置。在本文中，我们使用单一世界干预图和与混合图相关的模型的嵌套分解，从而非常简单地了解实验数据的现有识别理论。我们使用此视图来产生一般识别算法，以设置输入分布由一组任意的观察和实验分布组成，包括边际和条件分布。我们表明，对于某种类型（祖先边缘）的介入边际分布的问题，我们的算法已完成。
Causal inference quantifies cause-effect relationships by estimating counterfactual parameters from data. This entails using \emph{identification theory} to establish a link between counterfactual parameters of interest and distributions from which data is available. A line of work characterized non-parametric identification for a wide variety of causal parameters in terms of the \emph{observed data distribution}. More recently, identification results have been extended to settings where experimental data from interventional distributions is also available. In this paper, we use Single World Intervention Graphs and a nested factorization of models associated with mixed graphs to give a very simple view of existing identification theory for experimental data. We use this view to yield general identification algorithms for settings where the input distributions consist of an arbitrary set of observational and experimental distributions, including marginal and conditional distributions. We show that for problems where inputs are interventional marginal distributions of a certain type (ancestral marginals), our algorithm is complete.
