Conquering confounds and covariates in machine learning

As I tried to study impact of different deconfounding methods, as well as offer covariate regression ability in neuropredict, I realized the tools and methods I need to implement would be useful to broader machine learning and neuroscience community. Hence, I set aside a couple of weeks to review the relevant literature (esp. in the context of biomarkers and predictive modeling), which convinced me that there are still many open questions! For example, there is no consensus on 1) what really constitutes a confound?, 2) when should we try to defoncound it? and 2) how do we properly assess their impact? etc. That really convinced me even further we need a common, open and dependable library to conquer confounds in machine learning. So, I’d like to quickly announce the initial beta release of the beautiful python library called confounds.

Vision / Goals

The high-level and long-term goal 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).

More docs with usage examples are available in the repo.

Fuller documentation with API reference is here.

Contributors are most welcome! All the contributors will be credited and become authors in the paper to be published when the library is a sufficiently big and good shape.

Relevant references

This list will be updated regularly. If anything important or relevant or latest missing in the list below, let me know and I will update it. Stay tuned!

  • Goh, W. W. B., Wang, W., & Wong, L. (2017). Why Batch Effects Matter in Omics Data, and How to Avoid Them. Trends in Biotechnology35(6), 498–507. https://doi.org/10.1016/j.tibtech.2017.02.012
  • Görgen, K., Hebart, M. N., Allefeld, C., & Haynes, J.-D. (2018). The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. NeuroImage180, 19–30. https://doi.org/10.1016/j.neuroimage.2017.12.083
  • Hyatt, C. S., Owens, M. M., Crowe, M. L., Carter, N. T., Lynam, D. R., & Miller, J. D. (2019). The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. NeuroImage, 116225. https://doi.org/10.1016/j.neuroimage.2019.116225
  • Kaltenpoth, D., & Vreeken, J. (2019). We Are Not Your Real Parents: Telling Causal from Confounded using MDL. In Proceedings of the 2019 SIAM International Conference on Data Mining (Vol. 1–0, pp. 199–207). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.23
  • Linn, K. A., Gaonkar, B., Doshi, J., Davatzikos, C., & Shinohara, R. T. (2015). Addressing Confounding in Predictive Models with an Application to Neuroimaging. The International Journal of Biostatistics12(1), 31–44. https://doi.org/10.1515/ijb-2015-0030
  • Nguyen, H., Morris, R. W., Harris, A. W., Korgoankar, M. S., & Ramos, F. (2018). Correcting differences in multi-site neuroimaging data using Generative Adversarial Networks. ArXiv:1803.09375 [Cs]http://arxiv.org/abs/1803.09375
  • Parr, T., & Wilson, J. D. (2019). A Stratification Approach to Partial Dependence for Codependent Variables. ArXiv:1907.06698 [Cs, Stat]http://arxiv.org/abs/1907.06698
  • Rao, A., Monteiro, J. M., Mourão-Miranda, J., & Alzheimer’s Disease Initiative. (2017). Predictive Modelling using Neuroimaging Data in the Presence of Confounds. NeuroImagehttps://doi.org/10.1016/j.neuroimage.2017.01.066
  • Snoek, L., Miletić, S., & Scholte, H. S. (2019). How to control for confounds in decoding analyses of neuroimaging data. NeuroImage184, 741–760. https://doi.org/10.1016/j.neuroimage.2018.09.074

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