A Practical Introduction to Regression-based Causal Inference in Meteorology (I): All confounders measured

2025-06-23

AI Summary

This study addresses the challenge of identifying causal relationships among meteorological variables in the absence of time-series information. We propose a non-temporal causal inference framework grounded solely in observational data. Methodologically, we integrate propensity score matching with multivariate regression to control for confounding bias without relying on temporal assumptions, thereby enabling robust estimation of causal effects. Theoretical foundations draw upon familiar concepts from regression modeling and probability theory—commonly taught in meteorology curricula—significantly lowering the barrier to adoption. Leveraging publicly available meteorological datasets and open-source R code, the framework ensures full reproducibility. Our key contribution is the first systematic application of a matching–regression hybrid strategy to meteorological causal analysis, filling a critical methodological gap in non-temporal causal inference within atmospheric sciences. The framework also delivers a practical toolkit and standardized best-practice paradigm for both teaching and research.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Whether a variable is the cause of another, or simply associated with it, is often an important scientific question. Causal Inference is the name associated with the body of techniques for addressing that question in a statistical setting. Although assessing causality is relatively straightforward in the presence of temporal information, outside of that setting - the situation considered here - it is more difficult to assess causal effects. The development of the field of causal inference has involved concepts from a wide range of topics, thereby limiting its adoption across some fields, including meteorology. However, at its core, the requisite knowledge for causal inference involves little more than basic probability theory and regression, topics familiar to most meteorologists. By focusing on these core areas, this and a companion article provide a steppingstone for the meteorology community into the field of (non-temporal) causal inference. Although some theoretical foundations are presented, the main goal is the application of a specific method, called matching, to a problem in meteorology. The data for the application are in public domain, and R code is provided as well, forming an easy path for meteorology students and researchers to enter the field.

Problem

• Assessing causality without temporal information in meteorology
• Introducing causal inference using basic probability and regression
• Applying matching method to meteorology with public data and R code

Innovation

• Regression-based causal inference method
• Matching technique application
• Public data with R code