On Spurious Causality, CO2, and Global Temperature

TL;DR

This paper critiques previous methods for establishing causality between CO2 and global temperature, proposing a more reliable measure based on Vector Autoregressions that questions earlier findings.

Contribution

It introduces an improved information flow measure using VAR models and demonstrates that prior causality claims are not supported by data.

Findings

Most previous causality results cannot be corroborated.

The choice of CO2 series affects causal inference.

Modeling assumptions influence the credibility of causal estimates.

Abstract

Stips, Macias, Coughlan, Garcia-Gorriz, and Liang (2016, Nature Scientific Reports) use information flows (Liang, 2008, 2014) to establish causality from various forcings to global temperature. We show that the formulas being used hinges on a simplifying assumption that is nearly always rejected by the data. We propose an adequate measure of information flow based on Vector Autoregressions, and find that most results in Stips et al. (2016) cannot be corroborated. Then, it is discussed which modeling choices (e.g., the choice of CO2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.