PT  - JOURNAL ARTICLE
AU  - Alex E Yuan
AU  - Wenying Shou
TI  - Data-driven causal analysis of observational time series in ecology
AID  - 10.1101/2020.08.03.233692
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.08.03.233692
4099  - http://biorxiv.org/content/early/2021/07/22/2020.08.03.233692.short
4100  - http://biorxiv.org/content/early/2021/07/22/2020.08.03.233692.full
AB  - 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 StatementThe authors have declared no competing interest.