Conquering confounds and covariates in machine learning

Vision / Goals

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

1. visualize and establish the presence of confounds (e.g. quantifying confound-to-target relationships),

2. offer solutions to handle them appropriately via correction or removal etc, and

3. 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).

Documentation

https://raamana.github.io/confounds

Methods

Available:

• Residualize (e.g. via regression)

• Augment (include confounds as predictors)

To be added:

• Harmonize (correct batch effects via rescaling or normalization etc)

• Stratify (sub- or resampling procedures to minimize confounding)

• Utilities (Goals 1 and 3)

Citation

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

Contributors are most welcome.

confounds package is beta and under development. Your contributions of all kinds will be greatly appreciated. Learn how to contributes here at Contributing.