Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into non-overlapping groups such that outcomes of units in separate groups are independent. In this paper, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the network's outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii).

中文翻译：

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网络因果效应的自动 G 计算


推断平均因果效应的方法传统上依赖于两个关键假设：（i）一个单位接受的干预不能对另一个单位的结果产生因果影响； (ii) 单元可以被组织成不重叠的组，使得不同组中的单元的结果是独立的。在本文中，我们基于连接单元网络的单一实现开发了新的因果推断统计方法，假设（i）和（ii）都不成立。所提出的方法允许任意形式的干扰，其中一个单元的结果可能取决于其他单元接收到的干预，这些单元存在通过连接单元的网络路径；长程依赖性，即通过网络中的路径同样连接的任何两个单元的结果可能是相关的。在网络版本的一致性和没有未观察到的混杂的情况下，通过假设网络的结果、处理和协变量向量是某个链图模型的单一实现，可以使推理变得容易处理。该假设允许通过自动 g 计算算法来推断各种网络因果效应，自动 g 计算算法是 Robins 众所周知的 g 计算算法的网络推广，先前描述了在假设 (i) 和 (ii) 下进行因果推断。