causalis.shared.confounders_balance¶
Module Contents¶
Functions¶
Compute balance diagnostics for confounders between two treatment groups. |
API¶
- causalis.shared.confounders_balance.confounders_balance(data: causalis.dgp.causaldata.CausalData | causalis.dgp.multicausaldata.MultiCausalData, treatment_d_0: Optional[str] = None, treatment_d_1: Optional[str] = None) → pandas.DataFrame¶
Compute balance diagnostics for confounders between two treatment groups.
Produces a DataFrame containing expanded confounder columns (after one-hot encoding categorical variables if present) with:
confounders: name of the confounder
mean_d_0: mean value for control group (t=0)
mean_d_1: mean value for treated group (t=1)
abs_diff: abs(mean_d_1 - mean_d_0)
smd: standardized mean difference (Cohen’s d using pooled std)
ks_pvalue: p-value for the KS test (rounded to 5 decimal places, non-scientific)
Parameters
data : CausalData or MultiCausalData The causal dataset containing the dataframe and confounders. treatment_d_0 : str, optional For MultiCausalData, name of the first treatment column to compare. Mapped to output column
mean_d_0. treatment_d_1 : str, optional For MultiCausalData, name of the second treatment column to compare. Mapped to output columnmean_d_1.Returns
pd.DataFrame Balance table sorted by |smd| (descending).