Match the strength of your wording to the study design, not to the confidence of an AI summary. If a source only shows that two things occur together, describe an association rather than saying one caused the other.
Find the verbs that strengthen the claim
During editing, highlight “caused,” “drove,” “boosted,” “prevented,” “delivered” and “led to.” Also inspect headlines and recommendations: a careful paragraph can be undermined by a headline promising a result.
Replacing every strong verb with “may” is not enough. “May cause” still introduces a causal interpretation. When the evidence only compares groups, “was associated with” or “was higher among” is usually a more faithful description.
These are editorial safeguards, not an automatic verdict on any particular study.
Complete a causal-language worksheet
Record the following before approving a causal sentence:
- Proposed effect: what supposedly changed because of what?
- Design: experiment, observational comparison, before-and-after account or unresolved.
- Timing: did the proposed cause precede the outcome?
- Comparison: what happened without the intervention?
- Selection: how did participants enter each group?
- Alternatives: what other factors could explain the difference?
- Source wording: what conclusion do the authors actually support?
- Permitted sentence: your narrower publication wording.
A randomised design may strengthen causal interpretation, but still requires scrutiny of implementation, attrition and what was measured. Do not infer those details from an abstract that omits them.
Edit a hypothetical workplace finding
Imagine a fictional survey of 300 Indian office workers. People who report using AI assistants also report completing more weekly tasks. The survey does not assign tools or measure productivity before adoption.
Unsafe summary: “AI assistants increased worker productivity.”
Better summary: “In this hypothetical survey, AI-assistant users reported completing more tasks. The comparison does not establish whether tool use caused the difference.”
Possible alternatives include different job types, greater prior experience or more motivated workers selecting the tools. These are questions to investigate, not findings to assert. Evaluate the edit by checking whether it preserves the observed relationship without inventing an experiment.
Stop causal advice when design is unclear
If the methods cannot be inspected, remove the causal recommendation or seek an accessible methodological account. A small, consent-based evaluation using synthetic tasks can inform your own workflow, but cannot establish a national effect.
Before purchasing research access through the AI tools catalog, check access format and save terms. Marketplace account, code or token offers are not proof of official subscription equivalence; a purchase does not strengthen weak study design.
Sources and scope
Source material checked 29 October 2026; features/policies may change. The supplied Perplexity citations reference concerns links for inspecting cited material. Direct fetching was blocked; no causal-analysis capability is asserted. The worksheet and hypothetical survey are editorial advice.