Martin Huber, Jannis Kueck, Mara Mattes
arXiv 9 Jan 2026 · Econometrics
arXiv:2601.05728 · PDF · DOI · OpenAlex · Extracted main text
Interference or spillover effects arise when an individual's outcome (e.g., health) is influenced not only by their own treatment (e.g., vaccination) but also by the treatment of others, creating challenges for evaluating treatment effects. Exposure mappings provide a framework to study such interference by explicitly modeling how the treatment statuses of contacts within an individual's network affect their outcome. Most existing research relies on a priori exposure mappings of limited complexity, which may fail to capture the full range of interference effects. In contrast, this study applies a graph convolutional autoencoder to learn exposure mappings in a data-driven way, which exploit dependencies and relations within a network to more accurately capture interference effects. As our main contribution, we introduce a machine learning-based test for the validity of exposure mappings and thus test the identification of the direct effect. In this testing approach, the learned exposure mapping is used as an instrument to test the validity of a simple, user-defined exposure mapping. The test leverages the fact that, if the user-defined exposure mapping is valid (so that all interference operates through it), then the learned exposure mapping is statistically independent of any individual's outcome, conditional on the user-defined exposure mapping. We assess the finite-sample performance of this proposed validity test through a simulation study.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Leung, Michael P and Loupos, Pantelis (2022) Graph Neural Networks for Causal Inference Under Network Confounding | 0.928 | 4 | 3 | 100% |
| 2 | Martin Huber and Jannis Kueck (2023) Testing the identification of causal effects in observational data self | 0.909 | 8 | 3 | 75% |
| 3 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 4 | 3 | 75% |
| 4 | Nicolas Apfel and Julia Hatamyar and Martin Huber and Jannis Kueck (2024) Learning control variables and instruments for causal analysis in observational data self | 0.737 | 5 | 2 | 60% |
| 5 | Peter M. Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 2 | 100% |
| 6 | J Neyman (1959) Optimal asymptotic tests of composite statistical hypotheses | 0.644 | 2 | 2 | 100% |
| 7 | J M Robins and Andrea Rotnitzky (1995) Semiparametric Efficiency in Multivariate Regression Models with Missing Data | 0.644 | 2 | 2 | 100% |
| 8 | J. M. Robins and A. Rotnitzky and L.P. Zhao (1994) Estimation of Regression Coefficients When Some Regressors Are not Always Observed | 0.644 | 2 | 2 | 100% |
| 9 | Thomas N. Kipf and Max Welling (2016) Variational Graph Auto-Encoders | 0.511 | 2 | 1 | 100% |
| 10 | Ma, Jing and Wan, Mengting and Yang, Longqi and Li, Jundong and Hech… (2022) Learning Causal Effects on Hypergraphs | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.