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Abstract:
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Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) that the intervention received by one unit cannot causally influence the outcome of another; and (ii) that 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 may be dependent. Under the standard assumptions of consistency and no unobserved confounding in a network setting, 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. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network gener
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