Necessary conditions for valid causal inference from observational data.

Area of Science:

• Epidemiology and Biostatistics
• Causal Inference Methodology

Background:

• Observational studies heavily influence policy and public health decisions.
• Causal claims from observational data can be misleading if methodological standards are not met.

Purpose of the Study:

• To develop a comprehensive framework of necessary conditions for valid interventional causal inference from observational studies.
• To guide researchers in making trustworthy and decision-relevant causal claims.

Main Methods:

• Synthesized foundational causal theory, contemporary methods, and critiques of common practices.
• Integrated Potential Outcomes (PO), Structural Causal Models (SCM), Directed Acyclic Graphs (DAG), and machine-learning (ML).
• Developed a five-domain framework with criteria, rationale, and practical evaluation tools, including STROBE-style checklists.

Main Results:

• The framework organizes necessary conditions into five domains: conceptual clarity, study design, data analysis, interpretation, and robustness.
• Practical tools and checklists are provided for evaluating the fulfillment of these conditions.
• Demonstrates that meeting all conditions enables trustworthy causal claims.

Conclusions:

• Producing reliable causal inferences from observational data necessitates adherence to all five domains of the proposed framework.
• Modern methods and software facilitate the practical application of these rigorous standards.
• This framework enhances the validity and utility of observational studies for policy and public health.