1.4. doubleml.data.DoubleMLSSMData#

class doubleml.data.DoubleMLSSMData(data, y_col, d_cols, x_cols=None, z_cols=None, s_col=None, cluster_cols=None, use_other_treat_as_covariate=True, force_all_x_finite=True, force_all_d_finite=True)#

Double machine learning data-backend for Sample Selection Models.

DoubleMLSSMData objects can be initialized from pandas.DataFrame’s as well as numpy.ndarray’s.

Parameters:

data (pandas.DataFrame) – The data.
y_col (str) – The outcome variable.
d_cols (str or list) – The treatment variable(s).
s_col (str) – The selection variable for SSM models.
x_cols (None, str or list) – The covariates. If None, all variables (columns of data) which are neither specified as outcome variable y_col, nor treatment variables d_cols, nor instrumental variables z_cols, nor selection variable s_col are used as covariates. Default is None.
z_cols (None, str or list) – The instrumental variable(s). Default is None.
cluster_cols (None, str or list) – The cluster variable(s). Default is None.
use_other_treat_as_covariate (bool) – Indicates whether in the multiple-treatment case the other treatment variables should be added as covariates. Default is True.
force_all_x_finite (bool or str) – Indicates whether to raise an error on infinite values and / or missings in the covariates x. Possible values are: True (neither missings np.nan, pd.NA nor infinite values np.inf are allowed), False (missings and infinite values are allowed), 'allow-nan' (only missings are allowed). Note that the choice False and 'allow-nan' are only reasonable if the machine learning methods used for the nuisance functions are capable to provide valid predictions with missings and / or infinite values in the covariates x. Default is True.
force_all_d_finite (bool) – Indicates whether to raise an error on infinite values and / or missings in the treatment variables d. Default is True.

Examples

>>> from doubleml import DoubleMLSSMData
>>> from doubleml.irm.datasets import make_ssm_data
>>> # initialization from pandas.DataFrame
>>> df = make_ssm_data(return_type='DataFrame')
>>> obj_dml_data_from_df = DoubleMLSSMData(df, 'y', 'd', s_col='s')
>>> # initialization from np.ndarray
>>> (x, y, d, _, s) = make_ssm_data(return_type='array')
>>> obj_dml_data_from_array = DoubleMLSSMData.from_arrays(x, y, d, s=s)

Methods

`from_arrays`(x, y, d[, z, s, cluster_vars, ...])	Initialize `DoubleMLSSMData` object from `numpy.ndarray`'s.
`set_x_d`(treatment_var)	Function that assigns the role for the treatment variables in the multiple-treatment case.

Attributes

`all_variables`	All variables available in the dataset.
`binary_outcome`	Logical indicating whether the outcome variable is binary with values 0 and 1.
`binary_treats`	Series with logical(s) indicating whether the treatment variable(s) are binary with values 0 and 1.
`cluster_cols`	The cluster variable(s).
`cluster_vars`	Array of cluster variable(s).
`d`	Array of treatment variable; Dynamic! Depends on the currently set treatment variable; To get an array of all treatment variables (independent of the currently set treatment variable) call `obj.data[obj.d_cols].values`.
`d_cols`	The treatment variable(s).
`data`	The data.
`force_all_d_finite`	Indicates whether to raise an error on infinite values and / or missings in the treatment variables `d`.
`force_all_x_finite`	Indicates whether to raise an error on infinite values and / or missings in the covariates `x`.
`is_cluster_data`	Flag indicating whether this data object is being used for cluster data.
`n_cluster_vars`	The number of cluster variables.
`n_coefs`	The number of coefficients to be estimated.
`n_instr`	The number of instruments.
`n_obs`	The number of observations.
`n_treat`	The number of treatment variables.
`s`	Array of score or selection variable.
`s_col`	The selection variable.
`use_other_treat_as_covariate`	Indicates whether in the multiple-treatment case the other treatment variables should be added as covariates.
`x`	Array of covariates; Dynamic! May depend on the currently set treatment variable; To get an array of all covariates (independent of the currently set treatment variable) call `obj.data[obj.x_cols].values`.
`x_cols`	The covariates.
`y`	Array of outcome variable.
`y_col`	The outcome variable.
`z`	Array of instrumental variables.
`z_cols`	The instrumental variable(s).

classmethod DoubleMLSSMData.from_arrays(x, y, d, z=None, s=None, cluster_vars=None, use_other_treat_as_covariate=True, force_all_x_finite=True, force_all_d_finite=True)#

Initialize DoubleMLSSMData object from numpy.ndarray’s.

Parameters:

x (numpy.ndarray) – Array of covariates.
y (numpy.ndarray) – Array of the outcome variable.
d (numpy.ndarray) – Array of treatment variables.
s (numpy.ndarray) – Array of the selection variable for SSM models.
z (None or numpy.ndarray) – Array of instrumental variables. Default is None.
cluster_vars (None or numpy.ndarray) – Array of cluster variables. Default is None.
use_other_treat_as_covariate (bool) – Indicates whether in the multiple-treatment case the other treatment variables should be added as covariates. Default is True.
force_all_x_finite (bool or str) – Indicates whether to raise an error on infinite values and / or missings in the covariates x. Possible values are: True (neither missings np.nan, pd.NA nor infinite values np.inf are allowed), False (missings and infinite values are allowed), 'allow-nan' (only missings are allowed). Note that the choice False and 'allow-nan' are only reasonable if the machine learning methods used for the nuisance functions are capable to provide valid predictions with missings and / or infinite values in the covariates x. Default is True.
force_all_d_finite (bool) – Indicates whether to raise an error on infinite values and / or missings in the treatment variables d. Default is True.

Examples

>>> from doubleml import DoubleMLSSMData
>>> from doubleml.irm.datasets import make_ssm_data
>>> (x, y, d, _, s) = make_ssm_data(return_type='array')
>>> obj_dml_data_from_array = DoubleMLSSMData.from_arrays(x, y, d, s=s)

DoubleMLSSMData.set_x_d(treatment_var)#

Function that assigns the role for the treatment variables in the multiple-treatment case.

Parameters:: treatment_var (str) – Active treatment variable that will be set to d.