nnpiv.semiparametrics.DML_longterm

class nnpiv.semiparametrics.DML_longterm(Y, D, S, G, X1=None, V=None, v_values=None, include_V=True, ci_type='pointwise', loc_kernel='gau', bw_loc='silverman', estimator='MR', longterm_model='surrogacy', model1=<nnpiv.rkhs.rkhs2iv.RKHS2IVL2 object>, nn_1=False, sample_G='all', alpha=0.05, n_folds=5, n_rep=1, inner_n_jobs=None, random_seed=123, prop_score=sklearn.linear_model.LogisticRegression, CHIM=False, verbose=True, fitargs1=None, opts=None)[source]

Debiased Machine Learning for long-term causal analysis (DML-longterm) class with joint/sequential model fitting.

Parameters

  • Y (array-like) – Outcome variable.

  • D (array-like) – Treatment variable.

  • S (array-like) – Surrogate variable.

  • G (array-like) – Group variable.

  • X1 (array-like, optional) – Additional covariates.

  • V (array-like, optional) – Localization covariates.

  • v_values (array-like, optional) – Values for localization.

  • include_V (bool, optional) – Include localization covariates in the model.

  • ci_type (str, optional) – Type of confidence interval (‘pointwise’, ‘uniform’).

  • loc_kernel (str, optional) – Kernel for localization. Options include ‘gau’, ‘epa’, ‘uni’, ‘tri’, etc.

  • bw_loc (str, optional) – Bandwidth for localization.

  • estimator (str, optional) – Estimator type (‘MR’, ‘OR’, ‘hybrid’, ‘IPW’).

  • longterm_model (str, optional) – Model type for long-term analysis (‘surrogacy’, ‘latent_unconfounded’).

  • model1 (estimator /(list), optional) – Model for the outcome stage - Can be a joint or sequential estimator; if the latter a list must be given

  • nn_1 (bool /(list), optional) – Use neural network for the outcome stage.

  • sample_G (str, optional) – Estimate treatment effect for the indicated subpopulation (i.e., “G=0”, “G=1”, “all”)

  • alpha (float, optional) – Significance level for confidence intervals.

  • n_folds (int, optional) – Number of folds for estimation.

  • n_rep (int, optional) – Number of repetitions for estimation.

  • inner_n_jobs (int, optional) – Number of parallel jobs for inner fold processing. If None, defaults to min(n_folds, available_cores).

  • random_seed (int, optional) – Seed for random number generator.

  • prop_score (estimator, optional) – Model for propensity score.

  • CHIM (bool, optional) – Use CHIM method for dealing with limited overlap.

  • verbose (bool, optional) – Print progress information.

  • fitargs1 (dict, optional) – Arguments for fitting the outcome stage model.

  • opts (dict, optional) – Additional options.