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fix: correct typo 'seperately' to 'separately' in docstrings #873

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fix: correct typo 'seperately' to 'separately' in docstrings #873

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2 changes: 1 addition & 1 deletion causalml/inference/iv/drivlearner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
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2 changes: 1 addition & 1 deletion causalml/inference/meta/drlearner.py
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Expand Up @@ -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:
Expand Down
6 changes: 3 additions & 3 deletions causalml/inference/meta/slearner.py
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Expand Up @@ -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.
Expand Down Expand Up @@ -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].
Expand Down Expand Up @@ -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.
Expand Down
4 changes: 2 additions & 2 deletions causalml/inference/meta/tlearner.py
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Expand Up @@ -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.
Expand Down Expand Up @@ -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].
Expand Down
6 changes: 3 additions & 3 deletions causalml/inference/meta/xlearner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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:
Expand Down