🧠 Causal Language

Causal language refers to wording that suggests a cause-and-effect relationship between two variables.

Causal language is used when an author implies that one factor directly causes another — rather than merely being associated or correlated — even if the study design does not support such inference.

Examples:

• ❌ “Treatment leads to improved survival”
• ❌ “Dose escalation prevents local failure”

Correct alternatives:

• ✅ “Treatment was associated with improved survival”
• ✅ “Higher dose correlated with lower failure rates”

Using causal language in:

• Retrospective studies
• Observational studies without causal inference methods

… can:

• Overstate conclusions
• Mislead clinicians or policymakers
• Hide potential confounding factors or biases

• Randomized Controlled Trials (RCTs)
• Studies using robust causal inference tools:
  • Propensity score matching
  • Instrumental variable analysis
  • Directed acyclic graphs (DAGs)

If a study does not randomize exposure and lacks causal modeling, causal language should be avoided.