1.2. doubleml.data.DoubleMLClusterData#

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

Backwards compatibility wrapper for DoubleMLData with cluster_cols. This class is deprecated and will be removed in a future version. Use DoubleMLData with cluster_cols instead.

Methods

`from_arrays`(x, y, d, cluster_vars[, z, t, ...])	Initialize `DoubleMLClusterData` 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.
`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 DoubleMLClusterData.from_arrays(x, y, d, cluster_vars, z=None, t=None, s=None, use_other_treat_as_covariate=True, force_all_x_finite=True)#: Initialize DoubleMLClusterData from numpy.ndarray’s. This method is deprecated and will be removed with version 0.12.0, use DoubleMLData.from_arrays with cluster_vars instead.

DoubleMLClusterData.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.