statsmodels.treatment.treatment_effects.TreatmentEffect.ipw#

TreatmentEffect.ipw(return_results=True, effect_group='all', disp=False)[source]#

Inverse Probability Weighted treatment effect estimation

Parameters:

return_resultsbool: If True, then a results instance is returned. If False, just ATE, POM0 and POM1 are returned.
effect_group{“all”, 0, 1}: effectgroup determines for which population the effects are estimated. If effect_group is “all”, then sample average treatment effect and potential outcomes are returned. If effect_group is 1 or “treated”, then effects on treated are returned. If effect_group is 0, “untreated” or “control”, then effects on untreated, i.e. control group, are returned.
dispbool: Indicates whether the scipy optimizer should display the optimization results

Returns:

TreatmentEffectResults or tuple: Results instance if return_results is True, otherwise the tuple (ATE, POM0, POM1).