make_partially_linear_constant_dataset
extensions.synthetic_data.make_partially_linear_constant_dataset(n_obs=1000, ate=4.0, n_confounders=10, dgp='make_plr_CCDDHNR2018', seed=None, **doubleml_kwargs)
Generate a partially linear model data generating process with a constant treatment effect (ATE only). The outcome and treatment are both continuous. The dataset is generated using the make_plr_CCDDHNR2018
or make_plr_turrell2018
function from the doubleml
package.
Parameters
Name | Type | Description | Default |
---|---|---|---|
n_obs |
int | The number of observations to generate. Default is 1000. | 1000 |
ate |
float | The average treatment effect. Default is 4.0. | 4.0 |
n_confounders |
int | The number of confounders to generate. Default is 10. | 10 |
dgp |
str | The data generating process to use. Default is “make_plr_CCDDHNR20”. Can be “make_plr_CCDDHNR20” or “make_plr_turrell2018”. | 'make_plr_CCDDHNR2018' |
seed |
int | None | The seed to use for the random number generator. Default is None. | None |
**doubleml_kwargs |
Additional keyword arguments to pass to the data generating process. | {} |
Returns
Type | Description |
---|---|
pd.DataFrame | The generated dataset where y is the outcome, d is the treatment, and X are the covariates. |
float | The true average treatment effect. |