Related papers: Comparing Two Proxy Methods for Causal Identification

Comparing Two Proxy Methods for Causal Identification

URL: http://arxiv.org/abs/2512.00175v1
Date: Fri, 28 Nov 2025 19:24:11 GMT
Title: Comparing Two Proxy Methods for Causal Identification
Authors: Helen Guo, Elizabeth L. Ogburn, Ilya Shpitser,
Abstract summary: Two approaches to identifying causal effects in the presence of unmeasured variables are presented.<n>We provide insight into implications of the underlying assumptions, clarifying the scope of applicability for each method.
Score: 4.998584084394827
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable methods have emerged as a powerful solution. We contrast two major approaches in this framework: (1) bridge equation methods, which leverage solutions to integral equations to recover causal targets, and (2) array decomposition methods, which recover latent factors composing counterfactual quantities by exploiting unique determination of eigenspaces. We compare the model restrictions underlying these two approaches and provide insight into implications of the underlying assumptions, clarifying the scope of applicability for each method.

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