🎉 balance v0.15.0 is out!
What is balance?
balance is a Python package (from Meta) offering a simple workflow and methods for dealing with biased data samples when looking to infer from them to some population of interest. Biased samples often occur in survey statistics when respondents present non-response bias or surveys suffer from sampling bias (that are not missing completely at random). A similar issue arises in observational studies when comparing treated vs untreated groups, and in any data that suffers from selection bias.
Highlights from v0.15.0 (since v0.12.0):
✅ More control over modeling: The ability to run any sklearn model (instead of just LogisticRegression) to fit inverse-propensity-score weights. Plus formula-driven summaries and explicit missing-data handling.
✅ Stronger diagnostics: The way weights influence covariate imbalance can now be evaluated not just with ASMD (as before), but also with various distribution distance metrics (KLD, EMD, CVMD, KS).
✅ Reliable code: Test coverage was increased to 100%, with full type-checking across the whole codebase. Plus CLI enhancements and improved docs/tutorials.
