causallib.estimation.overlap_weights module

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Created on Jun 09, 2021

class causallib.estimation.overlap_weights.OverlapWeights(learner, use_stabilized=False)[source]

Bases: causallib.estimation.ipw.IPW

Implementation of overlap (propensity score) weighting:

https://www.tandfonline.com/doi/full/10.1080/01621459.2016.1260466

A method to balance observed covariates between treatment groups in observational studies. Down-weigh observations with extreme propensity and weigh up Put less importance to observations with extreme propensity scores, and put more emphasis on observations with a central tendency towards (i.e. overlapping propensity scores).

Each unit’s weight is proportional to the probability of that unit being assigned to the opposite group: w_i = 1 - Pr[A=a_i|Xi]

This method assumes only two treatment groups exist.

Parameters

learner – Initialized sklearn model.
use_stabilized (bool) – Whether to re-weigh the learned weights with the prevalence of the treatment. See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title

compute_weight_matrix(X, a, clip_min=None, clip_max=None, use_stabilized=None)[source]

Computes individual weight across all possible treatment values. w_ij = 1 - Pr[A=a_j | X_i] for all individual i and treatment j.

Parameters

X (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
a (pd.Series) – Treatment assignment of size (num_subjects,).
clip_min (None|float) – Lower bound for propensity scores. Better be left None.
clip_max (None|float) – Upper bound for propensity scores. Better be left None.
use_stabilized (None|bool) – Whether to re-weigh the learned weights with the prevalence of the treatment. This overrides the use_stabilized parameter provided at initialization. If True provided, but the model was initialized with use_stabilized=False, then prevalence is calculated from data at hand, rather than the prevalence from the training data. See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title

Returns

A matrix of size (num_subjects, num_treatments) with weight for every individual and every: treatment.

Return type

pd.DataFrame

stabilize_weights(a, weight_matrix, use_stabilized=False)[source]

Adjust sample weights according to class prevalence: Pr[A=a_i] * w_i

Parameters

weight_matrix (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
use_stabilized (None|bool) – Whether to re-weigh the learned weights with the prevalence of the treatment. This overrides the use_stabilized parameter provided at initialization. If True provided, but the model was initialized with use_stabilized=False, then prevalence is calculated from data at hand, rather than the prevalence from the training data. See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title

Returns

A matrix of size (num_subjects, num_treatments) with stabilized (if True): weight for every individual and every treatment.

Return type

pd.DataFrame