Auto-G-Computation of Causal Effects on a Network.

ABSTRACT: 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 nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, 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 th

SUBMITTER: Tchetgen Tchetgen EJ 

PROVIDER: S-EPMC8345318 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature