Confounding and Covariates
Use this section when your question is whether an adjusted analysis has handled or disclosed covariates clearly enough for interpretation and claim-bearing use.
Use this section when your question is whether an adjusted analysis has handled covariates clearly enough for a reader to understand what the reported result means.
This section is for covariate-adjustment interpretation and disclosure, not for choosing the primary statistical method or proving that confounding has been fully solved. If your first question is which model to run, start with Methods. If your first question is whether the figure is overall ready for claim-bearing use, start with Validity Score.
Is this the right section?
Use the table below to choose the leaf page in this category.
| If your question is... | Start here |
|---|---|
| Does this adjusted linear regression need an explicit covariate-adjustment disclosure before claim-bearing export? | Covariate Selection Audit |
If your main question is about residual problems, model stability, or overfitting rather than covariate adjustment, go to Model Diagnostics.
Support boundary
This category helps you review whether covariate adjustment has been disclosed clearly enough for interpretation; it is not a guarantee that Licklider has identified every source of confounding or verified that the chosen covariate set is scientifically complete.
Child pages
Start here
- Start with Covariate Selection Audit when you need to understand how Licklider currently handles covariate-adjustment disclosure in multi-predictor linear regression.
What this section does not cover
- Choosing the primary regression family or comparison method
- Full causal-identification strategy or protocol design
- A universal guarantee that confounding has been removed
- Residual diagnostics, overfitting risk, or model mis-specification as the primary question
Related
- Related links are registered in frontmatter and rendered below this page.