Confound Identification AI Prompt

Systematically identify the confounding variables and alternative explanations that threaten the validity of my study.

Study design: {{study_design}}
Main relationship of interest: {{main_relationship}} (X → Y)
Sample and context: {{sample_context}}

1. What is a confounder:
   A confounder is a variable Z that:
   - Is associated with the exposure/predictor X
   - Is associated with the outcome Y
   - Is NOT on the causal pathway between X and Y (not a mediator)
   If Z is not controlled for, the observed X-Y association will be biased.

2. Confounder brainstorming — work through these categories:
   a. Demographic confounders: age, sex, gender, race/ethnicity, socioeconomic status, education
   b. Behavioral confounders: lifestyle factors, prior behavior, health behaviors, technology use
   c. Temporal confounders: secular trends, seasonality, historical events that coincide with the study
   d. Selection confounders: how participants were recruited, who chose to participate, who dropped out
   e. Measurement confounders: how X was measured (method, instrument, assessor) may differ across levels of Y
   f. Domain-specific confounders: {{field}}-specific factors I should consider

3. For each identified confounder, assess:
   - Direction of bias: does this confounder inflate or deflate the X-Y association?
   - Magnitude of potential bias: is this likely to be a minor or major source of bias?
   - Measurability: can this confounder be measured and controlled?

4. Strategies to address each confounder:
   - Randomization: if using an experiment, randomization balances all confounders in expectation
   - Restriction: limit the sample to a narrow range of the confounder (e.g. study only one age group)
   - Matching: match treatment and control participants on key confounders
   - Statistical adjustment: include confounders as covariates in the model
   - Stratification: analyze subgroups separately
   - Instrumental variables / natural experiments: if confounders are unmeasurable

5. Residual confounding:
   Even after adjustment, some confounding will remain. State explicitly:
   - Which confounders cannot be measured and therefore cannot be controlled
   - The likely direction and magnitude of residual confounding
   - What this means for the causal interpretation of your results

Return: confounder list with bias direction and magnitude assessment, proposed control strategy per confounder, and a residual confounding statement suitable for a limitations section.