The high-level goals of this package is to develop high-quality library to conquer confounds and covariates in ML applications. By conquering, we mean methods and tools to
- visualize and establish the presence of confounds (e.g. quantifying confound-to-target relationships),
- offer solutions to handle them appropriately via correction or removal etc, and
- analyze the effect of the deconfounding methods in the processed data (e.g. ability to check if they worked at all, or if they introduced new or unwanted biases etc).
https://raamana.github.io/confounds
Available:
- Residualize (e.g. via regression)
- Augment (include confounds as predictors)
- Some utils
To be added:
- Harmonize (correct batch effects via rescaling or normalization etc)
- Stratify (sub- or re-sampling procedures to minimize confounding)
- Full set of utilities (Goals 1 and 3)
In a more schematic way:
If you found any parts of confounds to be useful in your research, directly or indirectly, I'd appreciate if you could cite the following:
- Pradeep Reddy Raamana (2020), "Conquering confounds and covariates in machine learning with the python library confounds", Version 0.1.1, Zenodo. http://doi.org/10.5281/zenodo.3701528
Your contributions of all kinds will be greatly appreciated. Learn how to contributes here at contrib_link.
All contributors making non-trivial contributions will be
- publicly and clearly acknowledged on the authors_link page
- become an author on the [software] paper to be published in the future.