A comparison of two causal methods in the context of climate analyses
Abstract
Correlation does not necessarily imply causation, and this is why causal methods have been developed
to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods,
namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence
(PCMCI) algorithm, and we apply them to four different artificial models of increasing complexity and one
real-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methods
are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display
some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller
number of variables and with PCMCI being best with a larger number of variables. Detecting causal links from
the fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study with
climate indices, both methods present some similarities and differences at monthly timescale. One of the key
differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–Southern
Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links,
in particular including nonlinear causal methods.
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Engineering Sciences [physics]Origin | Publisher files allowed on an open archive |
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