Time-varying confounding in epidemic intervention evaluations
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916907657986048 |
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| author | Zhang, Yichi Crawford, Forrest W. |
| author_facet | Zhang, Yichi Crawford, Forrest W. |
| contents | Estimating the causal effect of a time-varying public health intervention on the course of an infectious disease epidemic is an important methodological challenge. During the COVID-19 pandemic, researchers attempted to estimate the effects of social distancing policies, stay-at-home orders, school closures, mask mandates, vaccination programs, and many other interventions on population-level infection outcomes. However, measuring the effect of these interventions is complicated by time-varying confounding: public health interventions are causal consequences of prior outcomes and interventions, as well as causes of future outcomes and interventions. Researchers have shown repeatedly that neglecting time-varying confounding for individual-level longitudinal interventions can result in profoundly biased estimates of causal effects. However, the issue with time-varying confounding bias has often been overlooked in population-level epidemic intervention evaluations. In this paper, we explain why associational modeling to estimate the effects of interventions on epidemic outcomes based on observations can be prone to time-varying confounding bias. Using causal reasoning and model-based simulation, we show how directional bias due to time-varying confounding arises in associational modeling and the misleading conclusions it induces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-varying confounding in epidemic intervention evaluations Zhang, Yichi Crawford, Forrest W. Applications Estimating the causal effect of a time-varying public health intervention on the course of an infectious disease epidemic is an important methodological challenge. During the COVID-19 pandemic, researchers attempted to estimate the effects of social distancing policies, stay-at-home orders, school closures, mask mandates, vaccination programs, and many other interventions on population-level infection outcomes. However, measuring the effect of these interventions is complicated by time-varying confounding: public health interventions are causal consequences of prior outcomes and interventions, as well as causes of future outcomes and interventions. Researchers have shown repeatedly that neglecting time-varying confounding for individual-level longitudinal interventions can result in profoundly biased estimates of causal effects. However, the issue with time-varying confounding bias has often been overlooked in population-level epidemic intervention evaluations. In this paper, we explain why associational modeling to estimate the effects of interventions on epidemic outcomes based on observations can be prone to time-varying confounding bias. Using causal reasoning and model-based simulation, we show how directional bias due to time-varying confounding arises in associational modeling and the misleading conclusions it induces. |
| title | Time-varying confounding in epidemic intervention evaluations |
| topic | Applications |
| url | https://arxiv.org/abs/2508.13427 |