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How to use the causalml.dataset.simulate_nuisance_and_easy_treatment function in causalml

To help you get started, we’ve selected a few causalml examples, based on popular ways it is used in public projects.

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uber / causalml / tests / test_datasets.py View on Github

def test_get_synthetic_preds():
    preds_dict = get_synthetic_preds(synthetic_data_func=simulate_nuisance_and_easy_treatment,
                                     n=1000,
                                     estimators={'S Learner (LR)': LRSRegressor(), 'T Learner (XGB)': XGBTRegressor()})

    assert preds_dict['S Learner (LR)'].shape[0] == preds_dict['T Learner (XGB)'].shape[0]

uber / causalml / tests / test_datasets.py View on Github

def test_get_synthetic_auuc():
    preds_dict = get_synthetic_preds(synthetic_data_func=simulate_nuisance_and_easy_treatment,
                                     n=1000,
                                     estimators={'S Learner (LR)': LRSRegressor(), 'T Learner (XGB)': XGBTRegressor()})

    auuc_df = get_synthetic_auuc(preds_dict, plot=False)
    print(auuc_df)