Metalearner Sensitivity

Metalearner Sensitivity

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SensitivitySelectionBias

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

*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*

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SensitivitySubsetData

 SensitivitySubsetData (*args, **kwargs)

Takes a random subset of size sample_size of the data.

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SensitivityRandomReplace

 SensitivityRandomReplace (*args, **kwargs)

Replaces a random covariate with an irrelevant variable.

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SensitivityRandomCause

 SensitivityRandomCause (*args, **kwargs)

Adds an irrelevant random covariate to the dataframe.

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SensitivityPlaceboTreatment

 SensitivityPlaceboTreatment (*args, **kwargs)

Replaces the treatment variable with a new variable randomly generated.

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Sensitivity

 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/*

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alignment_att

 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)*

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one_sided_att

 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)*

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alignment

 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)*

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one_sided

 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)*