causalkit.eda.eda.CausalEDA.confounders_means#

CausalEDA.confounders_means()[source]#

Comprehensive confounders balance assessment with means by treatment group.

Returns a DataFrame with detailed balance information including: - Mean values of each confounder for control group (treatment=0) - Mean values of each confounder for treated group (treatment=1) - Absolute difference between treatment groups - Standardized Mean Difference (SMD) for formal balance assessment

This method provides a comprehensive view of confounder balance by showing the actual mean values alongside the standardized differences, making it easier to understand both the magnitude and direction of imbalances.

Returns:

DataFrame with confounders as index and the following columns: - mean_t_0: mean value for control group (treatment=0) - mean_t_1: mean value for treated group (treatment=1) - abs_diff: absolute difference abs(mean_t_1 - mean_t_0) - smd: standardized mean difference (Cohen’s d)

Return type:

pd.DataFrame

Notes

SMD values > 0.1 in absolute value typically indicate meaningful imbalance. Categorical variables are automatically converted to dummy variables.

Examples

>>> eda = CausalEDA(causal_data)
>>> balance = eda.confounders_means()
>>> print(balance.head())
             mean_t_0  mean_t_1  abs_diff       smd
confounders
age              29.5      31.2      1.7     0.085
income        45000.0   47500.0   2500.0     0.125
education         0.25      0.35      0.1     0.215
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