A Practical Introduction to Regression-based Causal Inference in Meteorology (II): Unmeasured confounders

Statistics > Applications [Submitted on 23 Jun 2025 (v1), last revised 18 Jun 2026 (this version, v2)] Title:A Practical Introduction to Regression-based Causal Inference in Meteorology (II): Unmeasured confounders View PDF HTML (experimental)Abstract:One obstacle to ``elevating'' correlation to causation is the phenomenon of confounding, i.e., when a correlation between two variables exists because both variables are in fact caused by a third variable, called a confounder. The situation where the confounders are measured is examined in an earlier, accompanying article. Here, it is shown that even when the confounding variables are not measured, under certain conditions it is still possible to estimate the causal effect via a regression-based method that uses the notion of instrumental variables. Using a meteorological data set, similar to that in the sister article, a number of different estimates of the causal effect are compared and contrasted. It is shown that the instrumental-variable estimates of causal effect depend on the choice of the instrumental variable, and that meteorological considerations are important in resolving the ambiguity. R code is provided for generating all of the results, and numerous directions for future work are outlined. Submission history From: Caren Marzban [view email][v1] Mon, 23 Jun 2025 13:54:52 UTC (59 KB) [v2] Thu, 18 Jun 2026 14:52:45 UTC (5,632 KB) References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.