  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.
