Abstract
Complex ecosystems are challenging to understand as they often defy manipulative experiments for practical or ethical reasons. In response, several fields have developed parallel approaches to infer causal relations from observational time series. Yet these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal inference approaches popular in ecological time series analysis: pairwise correlation, Granger causality, and state space reconstruction. For each, we ask what a method tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of causal inference methods, and point out how so-called “model-free” causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of causal inference approaches and encourage explicit statements of assumptions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
wenying.shou{at}gmail.com (WS)
Data accessibility: Simulation results can be reproduced using the source code, which will be made available on Github.
Abstract, main text, and supplementary information updated for improved clarity.