dualbounds.varite.VarITEDualBounds.cross_fit¶

VarITEDualBounds.cross_fit(nfolds: int = 5, suppress_warning: bool = False, verbose: bool = True, weight_by_propensities: bool = False)¶

Cross-fits the outcome model.

Parameters:¶

nfolds : int¶

Number of folds to use in cross-fitting.

suppress_warning : bool¶

If True, suppresses a potential warning about cross-fitting.

verbose : bool¶

If True, prints progress reports.

weight_by_propensities : bool¶

If True, when cross-fitting the outcome model, upweights observations with low propensity scores.

Returns:¶

• y0_dists (list) – list of batched scipy distributions whose shapes sum to n. the ith distribution is the out-of-sample estimate of the conditional law of \(Y_i(0) | X[i]\)

• y1_dists (list) – list of batched scipy distributions whose shapes sum to n. the ith distribution is the out-of-sample estimate of the conditional law of \(Y_i(1) | X[i]\)