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{HtmlEncodeMultiline(EmailPreheader)} | GRAPH NEURAL NETWORKS FOR CAUSAL INFERENCE UNDER NETWORK CONFOUNDING |
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| ABSTRACT This paper studies causal inference with observational network data. A challenging aspect of this setting is the possibility of interference in both potential outcomes and selection into treatment, for example due to peer effects in either stage. We therefore consider a nonparametric setup in which both stages are reduced forms of simultaneous-equations models. This results in high-dimensional network confounding, where the network and covariates of all units constitute sources of selection bias. The literature predominantly assumes that confounding can be summarized by a known, low-dimensional function of these objects, and it is unclear what selection models justify common choices of functions. We show that graph neural networks (GNNs) are well suited to adjust for high-dimensional network confounding. We establish a network ana-log of approximate sparsity under primitive conditions on interference. This demonstrates that the model has low-dimensional structure that makes estimation feasible and justifies the use of shallow GNN architectures. |
Keywords: Causal Inference, Unconfoundedness, Network Interference, Graph Neural Networks, Approximate Sparsity JEL Codes: C14, C31, C45 |
Click here to view the CV. Click here to view the paper. |
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PRESENTER Michael Leung University of California |
RESEARCH FIELDS Econometrics |
DATE: 30 August 2024 (Friday) |
VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903 |
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