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 the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks 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). Supplementary materials for this article are available online.

KEYWORDS:

• Direct effect
• Indirect effect
• Interference
• Network
• Spillover effect

Supplementary Materials

Appendix: Theorems, detailed proofs, and additional simulation results. (.zip file)

Code: Code for estimation and inference of network causal effects. To download, please visit: https://isabelfulcher.github.io/autoGnetworks/. (R)

Acknowledgments

We are grateful to Dr. Samuel R. Friedman at National Development and Research Institutes, Inc. for access to the Networks, Norms, and HIV/STI Risk Among Youth study data and contributions to the data application section.

Funding

This work was partially supported by National Institute of Allergy and Infectious Diseases of the National Institute of Health under Award Number T32AI007358, R01 AI104459-01A1, and R01 AI127271-01A1.

Additional information

Funding

This work was partially supported by National Institute of Allergy and Infectious Diseases of the National Institute of Health under Award Number T32AI007358, R01 AI104459-01A1, and R01 AI127271-01A1.