Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Causal assumptions and causal inference in ecological experiments.

Kaitlin Kimmel1, Laura E Dee2, Meghan L Avolio1

  • 1Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA.

Trends in Ecology & Evolution
|September 20, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Synthetic control methods enable stronger causal inference using participatory science data in cities.

Nature ecology & evolution·2026
Same author

Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects.

Nature ecology & evolution·2026
Same author

Best practices for moving from correlation to causation in ecological research.

Nature communications·2026
Same author

Riparian vegetation reduces coastal turbidity.

Communications sustainability·2026
Same author

Genetic Diversity and Population Structure in Cities Is Not Consistent Among Cosmopolitan Plant Species.

Molecular ecology·2026
Same author

Predicting missing links in food webs using stacked models and species traits.

Nature communications·2026

Ecologists need causal assumptions beyond randomization for valid experimental inferences. This framework helps design better experiments and bridges experimental and observational research.

Area of Science:

  • Ecology
  • Causal Inference
  • Experimental Design

Background:

  • Causal inferences in ecology often rely on treatment randomization.
  • However, formal causal assumptions are crucial but underutilized in ecological research.
  • Existing ecological studies face challenges in identifying and addressing causal inference limitations.

Purpose of the Study:

  • To review four key assumptions for causal inference from experiments in ecology.
  • To provide practical solutions for violations of these assumptions.
  • To offer a unified framework for experimental and observational studies.

Main Methods:

  • Literature review of causal inference assumptions.
  • Analysis of design-based and statistical solutions for assumption violations.
Keywords:
counterfactual causalityexcludabilityexclusion restrictioninterferencenoncompliancepotential outcomes

Related Experiment Videos

  • Synthesis of insights for ecological research practices.

    • Main Results:

    • Four core assumptions are necessary for robust causal inference in experiments.
    • Specific design and statistical methods can address violated assumptions.
    • The distinction between experimental and observational designs is blurred.

    Conclusions:

    • Ecologists require a deeper understanding of causal assumptions.
    • Implementing these assumptions enhances the rigor of ecological experiments.
    • This framework can integrate experimental and observational ecological scholarship.