one_sided[source]

one_sided(alpha, p, treatment)

One sided confounding function. Reference: Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. https://www.mattblackwell.org/files/papers/causalsens.pdf

Args: alpha (np.array): a confounding values vector p (np.array): a propensity score vector between 0 and 1 treatment (np.array): a treatment vector (1 if treated, otherwise 0)

alignment[source]

alignment(alpha, p, treatment)

Alignment confounding function. Reference: Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. https://www.mattblackwell.org/files/papers/causalsens.pdf

Args: alpha (np.array): a confounding values vector p (np.array): a propensity score vector between 0 and 1 treatment (np.array): a treatment vector (1 if treated, otherwise 0)

one_sided_att[source]

one_sided_att(alpha, p, treatment)

One sided confounding function for the average effect of the treatment among the treated units (ATT)

Reference: Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. https://www.mattblackwell.org/files/papers/causalsens.pdf

Args: alpha (np.array): a confounding values vector p (np.array): a propensity score vector between 0 and 1 treatment (np.array): a treatment vector (1 if treated, otherwise 0)

alignment_att[source]

alignment_att(alpha, p, treatment)

Alignment confounding function for the average effect of the treatment among the treated units (ATT)

Reference: Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. https://www.mattblackwell.org/files/papers/causalsens.pdf

Args: alpha (np.array): a confounding values vector p (np.array): a propensity score vector between 0 and 1 treatment (np.array): a treatment vector (1 if treated, otherwise 0)

class Sensitivity[source]

Sensitivity(df, inference_features, p_col, treatment_col, outcome_col, learner, *args, **kwargs)

A Sensitivity Check class to support Placebo Treatment, Irrelevant Additional Confounder and Subset validation refutation methods to verify causal inference.

Reference: https://github.com/microsoft/dowhy/blob/master/dowhy/causal_refuters/

class SensitivityPlaceboTreatment[source]

SensitivityPlaceboTreatment(*args, **kwargs) :: Sensitivity

Replaces the treatment variable with a new variable randomly generated.

class SensitivityRandomCause[source]

SensitivityRandomCause(*args, **kwargs) :: Sensitivity

Adds an irrelevant random covariate to the dataframe.

class SensitivityRandomReplace[source]

SensitivityRandomReplace(*args, **kwargs) :: Sensitivity

Replaces a random covariate with an irrelevant variable.

class SensitivitySubsetData[source]

SensitivitySubsetData(*args, **kwargs) :: Sensitivity

Takes a random subset of size sample_size of the data.

class SensitivitySelectionBias[source]

SensitivitySelectionBias(*args, confound='one_sided', alpha_range=None, sensitivity_features=None, **kwargs) :: Sensitivity

Reference:

[1] Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. https://www.mattblackwell.org/files/papers/causalsens.pdf

[2] Confouding parameter alpha_range using the same range as in: https://github.com/mattblackwell/causalsens/blob/master/R/causalsens.R