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

TL;DR

This paper demonstrates how regression-based causal inference using Instrumental Variables can estimate causal effects in meteorology even when confounders are unmeasured, showing consistency with measured confounder methods.

Contribution

It introduces a regression-based method employing Instrumental Variables to estimate causal effects with unmeasured confounders in meteorological data.

Findings

Instrumental Variable estimates align with measured confounder methods.

Unmeasured confounders can be effectively addressed with the proposed approach.

The method provides consistent causal effect estimates in meteorology.

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.