Necessary conditions for valid causal inference from observational data

Observational studies often guide policy, regulation, litigation, and public health decisions. However, causal claims from such studies can be misleading if key logical and methodological requirements are not met. We synthesized insights from foundational causal theory, contemporary methods, and critiques of common practices to develop a framework of necessary conditions for valid interventional causal inferences predicting the effects of changes in exposure on changes in risk. The framework organizes these conditions into five domains: (A) conceptual and definitional clarity, (B) study design prerequisites, (C) data analysis requirements for valid causal modeling and effect estimation, (D) interpretation and result integrity, and (E) robustness, generalizability, and external validity of results. Each domain includes criteria, rationale, and practical evaluation tools, integrating Potential Outcomes (PO), Structural Causal Models (SCM), Directed Acyclic Graphs (DAG), information-theoretic measures, and machine-learning (ML) approaches for heterogeneity. Summary tables and STROBE-style checklists translate the framework into actionable guidance. Producing trustworthy, decision-relevant causal claims from observational data requires meeting all necessary conditions across the five domains. Modern study designs, methods, and software make it increasingly practical to do so.