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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23291 |
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| _version_ | 1866910034244403200 |
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| author | Tang, Tommy Li, Xinran Li, Bo |
| author_facet | Tang, Tommy Li, Xinran Li, Bo |
| contents | The study of causal effects in the presence of unmeasured spatially varying confounders has garnered increasing attention. However, a general framework for identifiability, which is critical for reliable causal inference from observational data, has yet to be advanced. In this paper, we study a linear model with various parametric model assumptions on the covariance structure between the unmeasured confounder and the exposure of interest. We establish identifiability of the treatment effect for many commonly 20 used spatial models for both discrete and continuous data, under mild conditions on the structure of observation locations and the exposure-confounder association. We also emphasize models or scenarios where identifiability may not hold, under which statistical inference should be conducted with caution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23291 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Identifiability of Treatment Effects with Unobserved Spatially Varying Confounders Tang, Tommy Li, Xinran Li, Bo Methodology Statistics Theory 62F15 The study of causal effects in the presence of unmeasured spatially varying confounders has garnered increasing attention. However, a general framework for identifiability, which is critical for reliable causal inference from observational data, has yet to be advanced. In this paper, we study a linear model with various parametric model assumptions on the covariance structure between the unmeasured confounder and the exposure of interest. We establish identifiability of the treatment effect for many commonly 20 used spatial models for both discrete and continuous data, under mild conditions on the structure of observation locations and the exposure-confounder association. We also emphasize models or scenarios where identifiability may not hold, under which statistical inference should be conducted with caution. |
| title | Identifiability of Treatment Effects with Unobserved Spatially Varying Confounders |
| topic | Methodology Statistics Theory 62F15 |
| url | https://arxiv.org/abs/2602.23291 |