Predictive models aren't for causal inference.

Area of Science:

• Ecology
• Causal Inference
• Statistical Modeling

Background:

• Ecologists frequently utilize observational data to investigate causal relationships.
• Predictive modeling techniques, including information criterion-based model selection (e.g., AIC), are commonly employed for understanding ecological patterns.
• However, these predictive approaches are fundamentally unsuited for establishing causal conclusions.

Purpose of the Study:

• To differentiate between predictive and causal inference in ecological research.
• To demonstrate how predictive techniques can yield biased causal estimates.
• To advocate for the adoption of valid causal inference methodologies in ecology.

Main Methods:

• Highlighting the conceptual and practical differences between predictive modeling and causal inference.
• Illustrating the potential for bias when using predictive methods for causal questions.
• Introducing graphical causal inference methods, specifically the backdoor criterion.

Main Results:

• Predictive approaches, while useful for forecasting, do not support causal claims.
• The application of predictive techniques for causal inference can result in systematically biased estimates.
• The backdoor criterion provides a formal method for identifying causal relationships from observational data.

Conclusions:

• Ecologists should not rely on predictive modeling for causal inference.
• Adopting established causal inference methods is crucial for drawing valid conclusions from observational ecological data.
• The backdoor criterion offers a robust graphical approach for identifying causal effects in ecological studies.