extensions.synthetic_data.make_fully_hetereogenous_dataset
extensions.synthetic_data.make_fully_hetereogenous_dataset(n_obs=1000, n_confounders=5, ate=4.0, seed=None, **doubleml_kwargs)
Generate a interactive model data generating process with fully heterogenous treatment effects. The outcome is continuous and the treatment is binary. The dataset is generated using the make_confounded_irm_data function from the doubleml package. We enforce the additional “unobserved” confounder A to be zero for all observations, since confounding is captured in X.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
n_obs |
int | The number of observations to generate. Default is 1000. | 1000 |
n_confounders |
int | The number of confounders to generate. Default is 5. | 5 |
ate |
float | The average treatment effect. Default is 4.0. | 4.0 |
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. |
| pd.DataFrame | The true conditional average treatment effects. |
| float | The true average treatment effect. |