Identification of causal effects in case-control studies.

Area of Science:

• Epidemiology
• Causal Inference
• Biostatistics

Background:

• Case-control designs are fundamental yet frequently misunderstood epidemiological tools.
• Clarifying their application is essential for accurate causal inference.

Purpose of the Study:

• To re-examine classical concepts and assumptions of case-control studies.
• To determine conditions for endowing case-control study results with causal interpretation.

Main Methods:

• Analysis of classical concepts, assumptions, and principles in case-control studies.
• Identification of causal estimands under various sampling schemes (case-base, survivor, risk-set sampling).

Main Results:

• Established conditions for identifying intention-to-treat or per-protocol effects using case-control data.
• Provided a summary linking causal estimands to available data distributions under specific conditions.

Conclusions:

• Modern epidemiological methods can clarify assumptions for causal interpretation in case-control designs.
• This work aids in resolving misunderstandings and informs future research on causal inference in complex epidemiological settings.