statsmodels.treatment.treatment_effects.TreatmentEffect

class statsmodels.treatment.treatment_effects.TreatmentEffect(model, treatment, results_select=None, _cov_type='HC0', **kwds)[source]

Estimate average treatment effect under conditional independence

This class estimates treatment effect and potential outcome using 5 different methods, ipw, ra, aipw, aipw-wls, ipw-ra. Standard errors and inference are based on the joint GMM representation of selection or treatment model, outcome model and effect functions.

Parameters:
modelinstance of a model class

The model class should contain endog and exog for the outcome model.

treatmentndarray

indicator array for observations with treatment (1) or without (0)

results_selectresults instance

The results instance for the treatment or selection model.

_cov_type“HC0”

Internal keyword. The keyword oes not affect GMMResults which always corresponds to HC0 standard errors.

kwdskeyword arguments

currently not used

Notes

The outcome model is currently limited to a linear model based on OLS or WLS. Other outcome models, like Logit and Poisson, will become available in future.

Methods

aipw([return_results, disp])

ATE and POM from double robust augmented inverse probability weighting

aipw_wls([return_results, disp])

ATE and POM from double robust augmented inverse probability weighting.

from_data(endog, exog, treatment[, model])

create models from data

ipw([return_results, effect_group, disp])

Inverse Probability Weighted treatment effect estimation.

ipw_ra([return_results, effect_group, disp])

ATE and POM from inverse probability weighted regression adjustment.

ra([return_results, effect_group, disp])

Regression Adjustment treatment effect estimation.