Identifying Causal Effects Using a Single Proxy Variable
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914463321423872 |
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| author | Vollmer, Silvan Pfister, Niklas Weichwald, Sebastian |
| author_facet | Vollmer, Silvan Pfister, Niklas Weichwald, Sebastian |
| contents | Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under a completeness assumption on this mechanism, which we call Single Proxy Identifiability of Causal Effects or simply SPICE, we prove that causal effects are identifiable. We extend the proxy-based causal identifiability results by Kuroki and Pearl (2014); Pearl (2010) to higher dimensions, more flexible functional relationships and a broader class of distributions. Further, we develop a neural network based estimation framework, SPICE-Net, to estimate causal effects, which is applicable to both discrete and continuous treatments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Identifying Causal Effects Using a Single Proxy Variable Vollmer, Silvan Pfister, Niklas Weichwald, Sebastian Machine Learning Statistics Theory Methodology Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single, potentially multi-dimensional proxy variable of the unobserved confounder and that we know the mechanism that generates the proxy from the confounder. Under a completeness assumption on this mechanism, which we call Single Proxy Identifiability of Causal Effects or simply SPICE, we prove that causal effects are identifiable. We extend the proxy-based causal identifiability results by Kuroki and Pearl (2014); Pearl (2010) to higher dimensions, more flexible functional relationships and a broader class of distributions. Further, we develop a neural network based estimation framework, SPICE-Net, to estimate causal effects, which is applicable to both discrete and continuous treatments. |
| title | Identifying Causal Effects Using a Single Proxy Variable |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2604.09135 |