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, “treated” or “control”, then effects on untreated, i.e. control group, are returned.

dispbool

Indicates whether the scipy optimizer should display the optimization results

Returns:
TreatmentEffectsResults instance or tuple (ATE, POM0, POM1)

See also

TreatmentEffectsResults

Last update: Mar 18, 2024