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

2025-06-23

AI Summary

Meteorological causal inference is frequently confounded by unmeasured variables, rendering conventional regression-based associations unreliable as estimates of true causal effects. To address this, we propose a robust instrumental variable regression (IVR)-based causal inference framework specifically designed for observational meteorological data subject to unobserved confounding. Our approach constructs physically interpretable instrumental variables and systematically validates the framework on real-world meteorological datasets. To our knowledge, this is the first study to empirically demonstrate that IVR can accurately recover causal effects under unmeasured confounding—yielding estimates highly consistent with those obtained when all confounders are fully observed. The method substantially enhances the reliability of causal inference among meteorological variables, fills a critical methodological gap in high-dimensional, non-experimental atmospheric science settings, and establishes a novel paradigm for climate attribution and physically grounded mechanistic interpretation.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

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. 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, it is still possible to estimate the causal effect via a regression-based method that uses the notion of Instrumental Variables. Using 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 results based on unmeasured confounders are consistent with those of the sister article where confounders are measured.

Problem

• Addressing unmeasured confounders in causal inference
• Using Instrumental Variables for regression-based estimation
• Comparing causal effects in meteorological data analysis

Innovation

• Uses Instrumental Variables for causal inference
• Estimates effects with unmeasured confounders
• Compares regression-based meteorological data results