causallib.survival.WeightedStandardizedSurvival#

class WeightedStandardizedSurvival(weight_model, survival_model, stratify=True, outcome_covariates=None, weight_covariates=None)[source]#
Combines WeightedSurvival and StandardizedSurvival:

  1. Adjusts for treatment assignment by creating weighted pseudo-population (e.g., inverse propensity weighting).

  2. Computes parametric curve by fitting a time-varying hazards model that includes baseline covariates.

Parameters:

  • weight_model (WeightEstimator) – causallib compatible weight model (e.g., IPW)

  • survival_model (Any) –

    Two alternatives:
    1. Scikit-Learn estimator (needs to implement predict_proba) - compute parametric curve by fitting a

      time-varying hazards model that includes baseline covariates. Note that the model is fitted on a person-time table with all covariates, and might be computationally and memory expansive.

    2. lifelines RegressionFitter - use lifelines fitter to compute survival curves from baseline covariates,

      events and durations

    stratify (bool): if True, fit a separate model per treatment group outcome_covariates (array): Covariates to use for outcome model.

    If None - all covariates passed will be used. Either list of column names or boolean mask.

    weight_covariates (array): Covariates to use for weight model.

    If None - all covariates passed will be used. Either list of column names or boolean mask.

__init__(weight_model, survival_model, stratify=True, outcome_covariates=None, weight_covariates=None)[source]#
Combines WeightedSurvival and StandardizedSurvival:

  1. Adjusts for treatment assignment by creating weighted pseudo-population (e.g., inverse propensity weighting).

  2. Computes parametric curve by fitting a time-varying hazards model that includes baseline covariates.

Parameters:

  • weight_model (WeightEstimator) – causallib compatible weight model (e.g., IPW)

  • survival_model (Any) –

    Two alternatives:
    1. Scikit-Learn estimator (needs to implement predict_proba) - compute parametric curve by fitting a

      time-varying hazards model that includes baseline covariates. Note that the model is fitted on a person-time table with all covariates, and might be computationally and memory expansive.

    2. lifelines RegressionFitter - use lifelines fitter to compute survival curves from baseline covariates,

      events and durations

    stratify (bool): if True, fit a separate model per treatment group outcome_covariates (array): Covariates to use for outcome model.

    If None - all covariates passed will be used. Either list of column names or boolean mask.

    weight_covariates (array): Covariates to use for weight model.

    If None - all covariates passed will be used. Either list of column names or boolean mask.

fit(X, a, t, y, w=None, fit_kwargs=None)[source]#

Fits parametric models and calculates internal survival functions.

Parameters:

  • X (pandas.DataFrame) – Baseline covariate matrix of size (num_subjects, num_features).

  • a (pandas.Series) – Treatment assignment of size (num_subjects,).

  • t (pandas.Series) – Followup duration, size (num_subjects,).

  • y (pandas.Series) – Observed outcome (1) or right censoring event (0), size (num_subjects,).

  • w (pandas.Series) – NOT USED (for compatibility only) optional subject weights.

  • fit_kwargs (dict) – Optional kwargs for fit call of survival model

Returns:

self

estimate_individual_outcome(X, a, t, y=None, timeline_start=None, timeline_end=None)[source]#

Returns individual survival curves for each subject row in X/a/t

Parameters:

  • X (pandas.DataFrame) – Baseline covariate matrix of size (num_subjects, num_features).

  • a (pandas.Series) – Treatment assignment of size (num_subjects,).

  • t (pandas.Series) – Followup durations, size (num_subjects,).

  • y (Any | None) – NOT USED (for API compatibility only).

  • timeline_start (int) – Common start time-step. If provided, will generate survival curves starting from ‘timeline_start’ for all patients. If None, will predict from first observed event (t.min()).

  • timeline_end (int) – Common end time-step. If provided, will generate survival curves up to ‘timeline_end’ for all patients. If None, will predict up to last observed event (t.max()).

Returns:

with time-step index, subject IDs (X.index) as columns and point survival as entries

Return type:

pandas.DataFrame