What question did this study set out to answer?

The aim is to clarify how to infer causation from correlation in observational data using causal models.

Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data

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This article discusses causal inference using graphical models, emphasizing the importance of proper statistical control in observational data.

The aim is to clarify how to infer causation from correlation in observational data using causal models.
Introduced graphical causal models as tools for causal inference.
Explained the importance of controlling for third variables and the risks of improper control.
Discussed common pitfalls such as controlling for mediators and using unrepresentative samples.
Identified that controlling for certain third variables can distort estimates of causal effects.
Highlighted the necessity for domain knowledge when making causal inferences from observational data.

Julia M. Rohrer (2018) studied this question.

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This article discusses causal inference using graphical models, emphasizing the importance of proper statistical control in observational data.