Causal assumptions and causal inference in ecological experiments

Kaitlin Kimmel, Laura E. Dee, Meghan L. Avolio, Paul J. Ferraro

Research output: Contribution to journalReview articlepeer-review

Abstract

Causal inferences from experimental data are often justified based on treatment randomization. However, inferring causality from data also requires complementary causal assumptions, which have been formalized by scholars of causality but not widely discussed in ecology. While ecologists have recognized challenges to inferring causal relationships in experiments and developed solutions, they lack a general framework to identify and address them. We review four assumptions required to infer causality from experiments and provide design-based and statistically based solutions for when these assumptions are violated. We conclude that there is no clear demarcation between experimental and non-experimental designs. This insight can help ecologists design better experiments and remove barriers between experimental and observational scholarship in ecology.

Original languageEnglish
Pages (from-to)1141-1152
Number of pages12
JournalTrends in Ecology and Evolution
Volume36
Issue number12
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

Keywords

  • counterfactual causality
  • excludability
  • exclusion restriction
  • interference
  • noncompliance
  • potential outcomes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics

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