[x] Separate Chi-Square per feature for category-like fetaures
[ ] Metrics based on e.g. propensity score matching from causal inference. Well-matched samples are typically considered to have per variable differences of Δz_mean < 0.1 where z_mean is standardized mean for a sample. Nice example with balance plots in step 4 here.
[x] Refactor logistic detector to call sklearn directly and get typical ML scores: ROC score, APR, F1, etc
[x] Normalized GMM scores: check how normalization can make likelihood based scores (e.g. here)
Also consider adding:
Discrete KS test to cover all variable typesMetrics based on e.g. propensity score matching from causal inference. Well-matched samples are typically considered to have per variable differences ofΔz_mean < 0.1
wherez_mean
is standardized mean for a sample. Nice example with balance plots in step 4 here.