Bayer-Group/pybalance
A library for minimizing the effects of confounding covariates
This tool helps researchers, policy makers, and insurance companies analyze observational data to understand real cause-and-effect relationships. It takes your existing datasets, where direct experimentation isn't possible, and helps adjust for hidden factors (confounding variables) to provide a clearer picture of an intervention's true impact. This is for anyone needing to draw reliable causal conclusions from real-world, non-randomized data.
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Use this if you need to reliably determine the true impact of a variable when you can't conduct a randomized controlled experiment.
Not ideal if your primary need is for weighting methods rather than matching routines, as those are not yet implemented.
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Language
Jupyter Notebook
License
BSD-3-Clause
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Last pushed
May 28, 2025
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