Precision in Expression: How to State Association Not Causation in RWE—Clear Phrases and Disclaimers

Step 1 — Establish the conceptual distinction and stakes

In everyday language, people often conflate two different relationships between variables: association and causation. In the context of real-world evidence (RWE) and health economics and outcomes research (HEOR), making that distinction precise is not a pedantic exercise; it is central to responsible communication. An association is an observed relationship between two variables: when one changes, the other tends to change as well. This relationship can arise for many reasons beyond a direct cause-and-effect link. A causal claim, by contrast, asserts that changing one variable will produce a change in the other—implying an intervention or mechanism. Saying that A causes B carries a stronger claim and stronger implications for decision-making than saying A is associated with B.

Why does this distinction matter in HEOR and RWE? Stakeholders—regulators, payers, clinicians, and patients—use published evidence to make decisions about treatment reimbursement, coverage, clinical guidelines, and patient care. If language overstates the evidence (for example, presenting an association found in an observational study as if it were causal), stakeholders may act on weaker evidence than they realize. That can lead to inappropriate policy changes, misguided resource allocation, patient harm, and erosion of trust in research. Conversely, understating a robust causal inference can delay adoption of beneficial practices. Clear, accurate phrasing helps place findings in their proper evidentiary context and enables better, safer decisions.

Anchoring this distinction in a realistic RWE scenario makes the stakes concrete. Imagine an observational study comparing users of Drug A with nonusers. The study observes a lower hospitalization rate among Drug A users. Without strong causal justification, it is incorrect to say “Drug A reduces hospitalizations.” Instead, the accurate statement is “Use of Drug A was associated with a lower hospitalization rate.” That simple difference in wording communicates uncertainty about whether Drug A itself produced the difference or whether other factors (such as healthier patients preferentially receiving Drug A, or different patterns of care) explain the observed association.

In communicating RWE, precise wording also shapes how reviewers evaluate methods. Many experienced reviewers will read causal-sounding language as a stronger claim and will expect corresponding causal-identification strategies and sensitivity analyses. If those are absent, overstated language undermines credibility and can lead to rejection or heavy revision requests. Therefore, applying disciplined language conventions is both scientifically ethical and pragmatically advantageous for publication and policy uptake.

Step 2 — Map study designs to strength of causal claims

Observational RWE employs a variety of analytic methods intended to reduce bias and approximate causal inference when randomized controlled trials (RCTs) are infeasible. Each method has strengths and limitations; the choice of language should reflect those limits. Below is a compact map that links common methods to what claims they typically support and highlights the principal threats that should temper phrasing.

• Propensity score methods (matching, weighting, stratification): These techniques attempt to balance measured covariates between treated and comparison groups, producing groups that are similar on observed characteristics. When well-applied, propensity methods can support stronger associative statements and, cautiously, conditional causal interpretations under the assumption of no unmeasured confounding (the strong ignorability assumption). Typical threats: unmeasured confounding, imperfect covariate measurement, and variable selection bias. Language implication: avoid categorical causal verbs; prefer phrasing that reflects conditional associations and explicitly acknowledge the potential for residual confounding.

• Difference-in-differences (DiD): DiD uses temporal changes in outcomes between treated and control groups to estimate causal effects under the parallel trends assumption (that in the absence of treatment both groups would have followed similar outcome trajectories). DiD can strengthen causal interpretation when pre-treatment trends are parallel and when there is no time-varying confounding correlated with treatment. Typical threats: violations of parallel trends, anticipation effects, and concurrent policy or exposure changes. Language implication: frame findings as consistent with an effect given the assumption; emphasize checks on pre-trends and discuss possible violations.

• Instrumental variables (IV): IV methods exploit an instrument—an external variable that affects treatment but not the outcome except through treatment—to identify causal effects even in the presence of unmeasured confounding. IV can, in certain cases, provide quasi-experimental causal estimates, but validity hinges on strong and often untestable assumptions (relevance and exclusion restriction). Typical threats: weak instruments, instrument-outcome direct pathways, and heterogeneous treatment effects that complicate interpretation. Language implication: allow stronger causal wording only when instrument validity is convincingly argued and sensitivity analyses are supportive; otherwise, use guarded causal language and describe instrument assumptions explicitly.

• Regression adjustment / multivariable models: Regression controls for measured confounders by conditioning on covariates. These are foundational tools but are limited by the quality of measured covariates and model specification. Typical threats: omitted variable bias, model misspecification, and collider bias if conditioning on inappropriate variables. Language implication: use association language and explicitly note the dependence on measured covariates and potential for residual confounding.

Across all methods, time-varying confounding and selection bias are recurring concerns. The presence of such threats should prompt more cautious language. In short, the stronger the identifying assumptions and the more thoroughly they are tested, the more confidently a writer may use firmer phrasing—yet even then, explicit caveats should accompany causal-sounding statements.

Step 3 — Teach hedging language and templates

Precise hedging is the practical skill that translates methodological limitations into clear sentences. Hedging and qualifier phrases can be organized by degree of confidence.

• Strong association phrasing (higher confidence, but not unequivocal causation): “was associated with,” “was linked to,” “correlated with,” “showed a relationship with.” Use when methods reduce bias substantially and sensitivity analyses are supportive, but unmeasured confounding cannot be definitively excluded.

• Moderate phrasing (balanced caution): “is consistent with,” “is compatible with,” “may reflect,” “is suggestive of.” Use when results align with an expected causal effect but some alternative explanations remain plausible.

• Cautious phrasing (limited causal inference): “is associated with and may be influenced by,” “observed differences may reflect,” “we observed a relationship between X and Y, though causality cannot be established.” Use for standard observational analyses without strong identification strategies.

Sentence templates help standardize communication across abstracts, results, and conclusions while embedding method-specific caveats.

• Results sentence template (general): “After adjusting for [key covariates or using XXX method], [exposure] was associated with [direction and magnitude of outcome] (adjusted [measure] = X, 95% CI Y–Z).”

• Methods-caveated result: “Using propensity score [matching/weighting] to balance measured baseline covariates, [exposure] was associated with [outcome]; however, residual confounding by unmeasured factors cannot be ruled out.”

• Instrumental variable cautious claim: “An instrumental variable analysis, which leverages [instrument], yielded an effect estimate consistent with [direction of effect]; this estimate relies on the assumption that [instrument] affects the outcome only through [exposure].”

• Conclusions template: “Our findings suggest that [exposure] is associated with [outcome]; interpretation should consider [key limitations], and randomized studies or additional quasi-experimental evidence would strengthen causal claims.”

Transforming causal-sounding sentences involves substituting causal verbs and adding a succinct disclaimer: instead of “Drug A reduced hospitalizations,” write “Use of Drug A was associated with a lower rate of hospitalization; this observational study cannot establish causality, and residual confounding may partly explain the association.” That pattern—replace causal verbs, add method-specific caveats—is the core editing move.

Step 4 — Practice and application

When applying these phrasing principles, adopt a discipline-specific voice appropriate for HEOR audiences and decision-makers. Sensitivity analyses provide key language elements for expressing robustness and uncertainty. Common phrasing includes:

• “Results were robust to [describe sensitivity checks],” e.g., varying definitions of exposure, alternative model specifications, or exclusion of high-risk subgroups.

• “Sensitivity analyses suggest that [directional conclusion], although effect size was attenuated after [specific adjustment],” which communicates both robustness of direction and reduction in magnitude.

• To acknowledge residual confounding: “Despite adjustment for multiple measured covariates, the possibility of residual confounding remains.” If post-hoc analyses reduce effect size, language such as “the association was attenuated and became non-significant in sensitivity analyses addressing [specific concern], suggesting possible confounding” is precise and actionable.

A short checklist helps reviewers and editors ensure consistent, appropriate wording across a manuscript or policy brief. The checklist should include items such as:

• Did the title or abstract avoid unqualified causal verbs unless the study provides strong causal identification?
• Are primary result sentences phrased with association language and include key caveats?
• Are method-specific assumptions and key threats (unmeasured confounding, selection bias, time-varying confounding) clearly stated?
• Do sensitivity analyses appear in the abstract or main text when relevant, and are their implications for interpretation described?
• Are discipline-appropriate disclaimers present in the conclusion and implications sections to guide policymakers and clinicians?

Consistent use of this checklist during drafting and peer review reduces the likelihood that language overstates evidence and helps align reader expectations with the study’s true evidentiary strength.

Closing guidance

Clear, conditioned language—grounded in an explicit account of methods and their limitations—keeps RWE useful rather than misleading. Writers should aim for sentences that communicate what the evidence supports, the assumptions required for stronger claims, and how sensitivity analyses influence confidence. By systematically replacing causal verbs with association-focused phrasing, appending concise disclaimers tied to method-specific threats, and reporting the results of robustness checks, HEOR professionals make their work more credible and more useful to the diverse stakeholders who rely on RWE for decisions.