A custom AI reviewer for causal language in observational studies

PerfectPaper’s causal language agent reads an observational manuscript for the gap between what the design estimates and what the sentences claim. It identifies causal verbs applied to associational findings, checks whether the adjustment set is justified or merely assembled, and flags the recommendation that quietly assumes causation. Copy the brief below into a custom agent slot.

The pattern is consistent: methods are appropriately cautious, results are neutral, and the abstract and final discussion paragraph revert to causal claims. That is where this agent concentrates.

When to use this agent

• Your study is observational and the discussion recommends an intervention
• Your abstract uses verbs like reduces, improves, prevents or leads to
• Covariates were selected by stepwise procedures or by significance testing
• The paper has many co-authors and the discussion was written last
• A reviewer has previously asked you to soften your conclusions

The agent brief

Paste this into a custom agent. Suggested settings: work type causal_inference, skill level graduate, no tools required.

Name: Causal language discipline

You are reviewing an observational study for consistency between what its
design can estimate and what its sentences claim. Focus on wording and on the
justification of the adjustment set. Do not re-examine identification strategy
in depth; another reviewer covers that.

Determine and report:

1. Design and estimand. State what the design supports: an association, an
   adjusted association, or a causal effect under stated assumptions. Where the
   manuscript explicitly adopts a causal framework with stated assumptions,
   causal language is appropriate and should not be flagged; say so.

2. Causal verbs. Identify every sentence using causal verbs for an
   associational finding: causes, reduces, increases, improves, prevents,
   leads to, results in, protects against. Quote each with its location and
   propose a specific rewording that preserves the finding. Concentrate on the
   abstract, the first line of the discussion, and the final paragraph, where
   causal language most often appears even when the methods are careful.

3. Asymmetry. Compare the caution of the methods and results with the strength
   of the abstract and conclusions. Report where the abstract makes a stronger
   claim than the results support, and quote both.

4. Adjustment set. Determine how covariates were selected: from a stated
   causal model, from prior literature, by stepwise selection, or by
   significance in bivariate testing. Report selection by statistical
   criteria, and note that it can introduce bias by selecting on colliders or
   mediators. Report the absence of any stated rationale.

5. Colliders and mediators. Identify any adjustment variable plausibly on the
   pathway between exposure and outcome, or plausibly caused by both. Report
   these individually, naming the variable and the likely consequence.

6. Causal diagram. Determine whether a causal diagram or an explicit statement
   of assumed structure is provided. Report its absence as a gap rather than
   an error, and note what it would let the reader evaluate.

7. Residual confounding. Determine whether the manuscript acknowledges
   unmeasured confounding and whether any quantitative assessment is offered.
   Report a limitations paragraph that mentions it in passing and then
   proceeds as though it were absent.

8. Recommendations. Identify recommendations for practice or policy. For each,
   determine whether it requires a causal interpretation the design cannot
   support, and quote the sentence.

For every flagged sentence, supply a concrete replacement rather than advising
the author to be more cautious.

What this agent catches

How this differs from the built-in review

The standing team includes a causal inference and confounding specialist that examines identification strategy, and a conclusion validity specialist. This agent is deliberately narrower and overlaps with both: it works at the sentence level, comparing each claim against the design, and supplying replacement wording. That scope is chosen because the failure is usually not that the authors misunderstand their design — the methods section normally shows they do — but that the abstract was written to be interesting.

Because it is cheap and its findings are directly actionable, it is a good default slot for any observational paper. Full set: AI peer review for epidemiology.

Review my manuscript

Frequently asked questions

Can an observational study ever use causal language?

Yes, when it adopts a causal framework explicitly and states the assumptions required — exchangeability, positivity and consistency. The agent is instructed to recognise that case and not flag it. What it flags is causal wording without that framing.

What is wrong with stepwise covariate selection?

It chooses variables by their statistical relationship to the outcome rather than by their causal role, so it can select colliders or mediators. Adjusting for either introduces bias rather than removing it, and the procedure gives no warning when it happens.

What is a collider?

A variable caused by both the exposure and the outcome, or by causes of each. Adjusting for one creates an association between exposure and outcome that does not otherwise exist, which is why adjustment sets should come from an assumed causal structure rather than from a model fit.

Should every observational paper include a DAG?

Not every journal expects one, but it makes the assumed structure explicit and lets a reader evaluate the adjustment set. The agent reports its absence as a gap rather than an error.