Using Embeddings to Correct for Unobserved Confounding in Networks

Victor Veitch; Yixin Wang; David M. Blei

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arXiv · 1902.04114

Using Embeddings to Correct for Unobserved Confounding in Networks

Victor Veitch·Yixin Wang·David M. Blei

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Abstract

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 semi-supervised prediction. We show that the method yields valid inferences under suitable (weak) conditions on the quality of the predictive model. We validate the method with experiments on a semi-synthetic social network dataset. Code is available at github.com/vveitch/causal-network-embeddings.

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BibTeX RIS

Victor Veitch, Yixin Wang, David M. Blei. 2019-02-11. Using Embeddings to Correct for Unobserved Confounding in Networks. https://arxiv.org/abs/1902.04114

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