doubleml.utils.DoubleMLBLP#

class doubleml.utils.DoubleMLBLP(orth_signal, basis, is_gate=False)#

Best linear predictor (BLP) for DoubleML with orthogonal signals. Manily used for CATE and GATE estimation for IRM models.

Parameters:

orth_signal (numpy.array) – The orthogonal signal to be predicted. Has to be of shape (n_obs,), where n_obs is the number of observations.
basis (pandas.DataFrame) – The basis for estimating the best linear predictor. Has to have the shape (n_obs, d), where n_obs is the number of observations and d is the number of predictors.
is_gate (bool) – Indicates whether the basis is constructed for GATEs (dummy-basis). Default is False.

Methods

`confint`([basis, joint, level, n_rep_boot])	Confidence intervals for the BLP model.
`fit`()	Estimate DoubleMLBLP models.

Attributes

`basis`	Basis.
`blp_model`	Best-Linear-Predictor model.
`blp_omega`	Covariance matrix.
`orth_signal`	Orthogonal signal.
`summary`	A summary for the best linear predictor effect after calling `fit()`.

DoubleMLBLP.confint(basis=None, joint=False, level=0.95, n_rep_boot=500)#

Confidence intervals for the BLP model.

Parameters:

basis (pandas.DataFrame) – The basis for constructing the confidence interval. Has to have the same form as the basis from the construction. If None is passed, if the basis is constructed for GATEs, the GATEs are returned. Else, the confidence intervals for the basis coefficients are returned (with pointwise cofidence intervals). Default is None.
joint (bool) – Indicates whether joint confidence intervals are computed. Default is False.
level (float) – The confidence level. Default is 0.95.
n_rep_boot (int) – The number of bootstrap repetitions (only relevant for joint confidence intervals). Default is 500.

Returns:

df_ci – A data frame with the confidence interval(s).

Return type:

pd.DataFrame

DoubleMLBLP.fit()#

Estimate DoubleMLBLP models.