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Reducing bias in experimental ecology through directed acyclic graphs.

Suchinta Arif¹, Melanie Duc Bo Massey¹

¹Department of Biology Dalhousie University 1355 Oxford Street Halifax Nova Scotia B3H 4R2 Canada.

Ecology and Evolution

|April 3, 2023

Summary

Ecologists can improve causal inference in experiments by understanding and avoiding biases. Applying structural causal models (SCMs) with directed acyclic graphs (DAGs) helps ensure accurate ecological research and valid conclusions.

Keywords:

causal inference collider bias confounding bias directed acyclic graphs (DAGs)external validity overcontrol bias randomized control trials (RCTs)

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Area of Science:

Ecology
Causal Inference
Experimental Design

Background:

Randomized control trials (RCTs) are fundamental for ecological research.
However, RCTs rely on underlying causal assumptions that require careful justification.
Biases like confounding, overcontrol, and collider bias can compromise experimental validity.

Purpose of the Study:

To highlight potential biases in ecological experiments.
To introduce the structural causal model (SCM) framework for bias mitigation.
To demonstrate the application of directed acyclic graphs (DAGs) in ecological study design and analysis.

Main Methods:

Utilizing ecological examples to illustrate common biases in experimental setups.
Applying the structural causal model (SCM) framework and directed acyclic graphs (DAGs).
Demonstrating how graphical rules from SCMs can identify and remove biases.

Main Results:

Biases such as confounding, overcontrol, and collider bias can occur in ecological RCTs.
The SCM framework, using DAGs, provides a method to visualize and address these biases.
DAGs facilitate proper study design and statistical analysis for more accurate causal estimates.

Conclusions:

Ecological experiments, including RCTs, require careful design and analysis to avoid biases.
The SCM framework and DAGs are valuable tools for ecologists to meet causal assumptions.
Applying DAGs enhances the validity of causal inference in ecological research.