Foundations and Future Directions for Causal Inference in Ecological Research.

Area of Science:

• Ecology
• Causal Inference
• Observational and Experimental Data Analysis

Background:

• Ecology frequently addresses causal questions, traditionally relying on experiments.
• Emerging data streams and real-world applicability challenges necessitate advanced methods.
• Existing causal inference frameworks from other disciplines offer potential solutions for ecological research.

Purpose of the Study:

• To introduce and explain causal inference approaches for ecological research.
• To bridge the disciplinary jargon gap and provide a comprehensive resource for ecologists.
• To advocate for the integration of causal inference into ecological best practices.

Main Methods:

• Discussion of counterfactual causal inference frameworks.
• Application of causal inference in experimental and quasi-experimental designs.
• Analysis of data requirements, assumptions, biases, and validity trade-offs (internal vs. external).

Main Results:

• Causal inference methods can enhance ecological research using diverse data.
• Study designs often prioritize internal validity over generalizability.
• Foundational papers and key challenges are identified.

Conclusions:

• Ecologists can leverage causal inference to answer complex questions.
• Further integration with synthesis science and meta-analysis is recommended.
• Establishing collective best practices for causal inference in ecology is crucial.