[Causality and prediction: differences and points of contact].

Area of Science:

• Methodology
• Statistical modeling
• Causal inference

Background:

• Distinguishing causal variables from predictive variables is crucial in scientific research.
• Misinterpreting associations can lead to flawed conclusions in both causal and predictive analyses.

Purpose of the Study:

• To delineate the fundamental differences between variables with causal roles and those used solely for prediction.
• To examine the roles of association, temporal precedence, and biases in causal inference and predictive modeling.
• To review study designs and illustrate common errors in handling causality and prediction.

Main Methods:

• Conceptual review of causal inference principles.
• Comparative analysis of predictive modeling techniques.
• Illustration of methodological pitfalls through examples.

Main Results:

• Causal variables require establishing temporal precedence and ruling out confounders, unlike predictive variables.
• Biases significantly impact both causal inference and predictive accuracy.
• Common errors include confusing correlation with causation and overfitting predictive models.

Conclusions:

• Careful study design is essential to differentiate causality from prediction.
• Understanding methodological nuances prevents common errors in data analysis.
• Accurate causal inference and robust prediction require distinct approaches and validation.