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

TL;DR

This paper introduces regression-based causal inference, specifically matching, to meteorology, emphasizing practical application with publicly available data and R code, to help meteorologists assess causality beyond correlation.

Contribution

It provides a practical guide for meteorologists to apply causal inference methods, focusing on matching, with accessible data and code, bridging a gap in the field.

Findings

Matching effectively estimates causal effects in meteorology.

Accessible tools facilitate adoption of causal inference techniques.

The approach clarifies causal relationships beyond simple associations.

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.