diff --git a/causalml/inference/iv/drivlearner.py b/causalml/inference/iv/drivlearner.py index bdc6d21d..bad03a2f 100644 --- a/causalml/inference/iv/drivlearner.py +++ b/causalml/inference/iv/drivlearner.py @@ -352,7 +352,7 @@ def fit_predict( return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (str): whether to output progress logs seed (int): random seed for cross-fitting Returns: diff --git a/causalml/inference/meta/drlearner.py b/causalml/inference/meta/drlearner.py index 300a1aa4..49f0382a 100644 --- a/causalml/inference/meta/drlearner.py +++ b/causalml/inference/meta/drlearner.py @@ -274,7 +274,7 @@ def fit_predict( return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (str): whether to output progress logs seed (int): random seed for cross-fitting Returns: diff --git a/causalml/inference/meta/slearner.py b/causalml/inference/meta/slearner.py index 796ac11f..e9be74b0 100644 --- a/causalml/inference/meta/slearner.py +++ b/causalml/inference/meta/slearner.py @@ -97,7 +97,7 @@ def predict( X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. @@ -159,7 +159,7 @@ def fit_predict( return_ci (bool, optional): whether to return confidence intervals n_bootstraps (int, optional): number of bootstrap iterations bootstrap_size (int, optional): number of samples per bootstrap - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment]. @@ -334,7 +334,7 @@ def predict( X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. diff --git a/causalml/inference/meta/tlearner.py b/causalml/inference/meta/tlearner.py index 04ca796f..e41d8cec 100644 --- a/causalml/inference/meta/tlearner.py +++ b/causalml/inference/meta/tlearner.py @@ -104,7 +104,7 @@ def predict( X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. @@ -162,7 +162,7 @@ def fit_predict( return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (str): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment]. diff --git a/causalml/inference/meta/xlearner.py b/causalml/inference/meta/xlearner.py index 88b5dc1d..658c99aa 100644 --- a/causalml/inference/meta/xlearner.py +++ b/causalml/inference/meta/xlearner.py @@ -164,7 +164,7 @@ def predict( p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. @@ -238,7 +238,7 @@ def fit_predict( return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately verbose (str): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment] @@ -586,7 +586,7 @@ def predict( p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. - return_components (bool, optional): whether to return outcome for treatment and control seperately + return_components (bool, optional): whether to return outcome for treatment and control separately return_p_score (bool, optional): whether to return propensity score verbose (bool, optional): whether to output progress logs Returns: