We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks admit high-quality embedding models that can be used for this semisupervised prediction. We show that the method yields valid inferences under suitable (weak) conditions on the quality of the predictive model.

This paper addresses the use of neural networks for the estimation of treatment effects from observational data.

We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks admit high-quality embedding models that can be used for this semisupervised prediction. We show that the method yields valid inferences under suitable (weak) conditions on the quality of the predictive model.

This paper addresses the use of neural networks for the estimation of treatment effects from observational data.