//  [python]
def test_missing2(self):
        n = 100
        np.random.seed(123)
        X = np.random.normal(size=(n, 1))
        W = np.random.normal(size=(n, 5))
        T = np.random.binomial(1, scipy.special.expit(X[:, 0] + np.random.normal(size=(n,))))
        y = (1 + 0.5 * X[:, 0]) * T + X[:, 0] + np.random.normal(size=(n,))
        mask = np.random.rand(*W.shape) < 0.05
        W_missing = W.copy()
        W_missing[mask] = np.nan
        mask = np.random.rand(*X.shape) < 0.05
        X_missing = X.copy()
        X_missing[mask] = np.nan
        Z = np.random.binomial(1, 0.5, size=(n,))

        clsf = make_pipeline(Imputer(strategy='mean'), LogisticRegression())
        regr = make_pipeline(Imputer(strategy='mean'), Lasso())
        model_final = make_pipeline(Imputer(strategy='mean'), LinearRegression())
        param_model_final = ParametricModelFinalForMissing(make_pipeline(
            Imputer(strategy='mean'), StatsModelsLinearRegression(fit_intercept=False)))
        non_param_model_final = NonParamModelFinal(make_pipeline(Imputer(strategy='mean'), Lasso()))
        non_param_model_final_t = NonParamModelFinal(make_pipeline(
            Imputer(strategy='mean'), LogisticRegression()))
        discrete_treatment = True
        discrete_instrument = True

        # test X, W only
        x_w_missing_models = [
            NonParamDML(model_y=regr, model_t=clsf, model_final=non_param_model_final,
                        discrete_treatment=discrete_treatment, allow_missing=True),
            DML(model_y=regr, model_t=clsf, discrete_treatment=discrete_treatment,
                model_final=param_model_final, allow_missing=True),
            DMLIV(model_y_xw=regr, model_t_xw=clsf, model_t_xwz=clsf,
                  model_final=param_model_final, discrete_treatment=discrete_treatment,
                  discrete_instrument=discrete_instrument, allow_missing=True),
            NonParamDMLIV(model_y_xw=regr, model_t_xw=clsf, model_t_xwz=clsf,
                          model_final=non_param_model_final, discrete_treatment=discrete_treatment,
                          discrete_instrument=discrete_instrument, allow_missing=True),
            DRLearner(model_propensity=clsf, model_regression=regr,
                      model_final=model_final, allow_missing=True)
        ]

        # test W only
        w_missing_models = [
            DRIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf, model_tz_xw=regr,
                 discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument,
                 prel_cate_approach='driv', projection=False, allow_missing=True),
            DRIV(model_y_xw=regr, model_t_xw=clsf, model_t_xwz=clsf, model_tz_xw=regr,
                 discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument,
                 prel_cate_approach='driv', projection=True, allow_missing=True),
            DRIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf, model_t_xwz=clsf, model_tz_xw=regr,
                 discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument,
                 prel_cate_approach='dmliv', projection=False, allow_missing=True),
            DRIV(model_y_xw=regr, model_t_xw=clsf, model_t_xwz=clsf, model_tz_xw=regr,
                 discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument,
                 prel_cate_approach='dmliv', projection=True, allow_missing=True),
            IntentToTreatDRIV(model_y_xw=regr, model_t_xwz=clsf, prel_cate_approach='driv',
                              model_final=model_final, allow_missing=True),
            IntentToTreatDRIV(model_y_xw=regr, model_t_xwz=clsf, prel_cate_approach='dmliv',
                              model_final=model_final, allow_missing=True),
            LinearDML(model_y=regr, model_t=clsf, discrete_treatment=True, allow_missing=True),
            SparseLinearDML(model_y=regr, model_t=clsf, discrete_treatment=True, allow_missing=True),
            KernelDML(model_y=regr, model_t=clsf, discrete_treatment=True, allow_missing=True),
            CausalForestDML(model_y=regr, model_t=clsf, discrete_treatment=True, allow_missing=True),
            LinearDRLearner(model_propensity=clsf, model_regression=regr, allow_missing=True),
            SparseLinearDRLearner(model_propensity=clsf, model_regression=regr, allow_missing=True),
            ForestDRLearner(model_propensity=clsf, model_regression=regr, allow_missing=True),
            OrthoIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf,
                    discrete_treatment=True, discrete_instrument=True, allow_missing=True),
            LinearDRIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf, model_tz_xw=regr,
                       prel_cate_approach='driv', discrete_treatment=True, discrete_instrument=True,
                       allow_missing=True),
            SparseLinearDRIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf, model_tz_xw=regr,
                             prel_cate_approach='driv', discrete_treatment=True, discrete_instrument=True,
                             allow_missing=True),
            ForestDRIV(model_y_xw=regr, model_t_xw=clsf, model_z_xw=clsf, model_tz_xw=regr,
                       prel_cate_approach='driv', discrete_treatment=True, discrete_instrument=True,
                       allow_missing=True),
            LinearIntentToTreatDRIV(model_y_xw=regr, model_t_xwz=clsf,
                                    prel_cate_approach='driv', allow_missing=True),
            DMLOrthoForest(model_T=clsf, model_Y=regr, model_T_final=non_param_model_final_t,
                           model_Y_final=non_param_model_final, discrete_treatment=True, allow_missing=True),
            DROrthoForest(propensity_model=clsf, model_Y=regr, propensity_model_final=non_param_model_final_t,
                          model_Y_final=non_param_model_final, allow_missing=True),
        ]

        metalearners = [
            SLearner(overall_model=regr, allow_missing=True),
            TLearner(models=regr, allow_missing=True),
            XLearner(models=regr, propensity_model=clsf, cate_models=regr, allow_missing=True),
            DomainAdaptationLearner(models=regr, final_models=regr,
                                    propensity_model=clsf, allow_missing=True)
        ]

        for est in x_w_missing_models:
            with self.subTest(est=est, kind='missing X and W'):

                if 'Z' in inspect.getfullargspec(est.fit).kwonlyargs:
                    include_Z = True
                else:
                    include_Z = False

                data_dict = create_data_dict(y, T, X, X_missing, W, W_missing, Z,
                                             X_has_missing=True, W_has_missing=True, include_Z=include_Z)
                est.fit(**data_dict)
                # dowhy does not support missing values in X
                self.assertRaises(ValueError, est.dowhy.fit, **data_dict)

                # assert that fitting with missing values fails when allow_missing is False
                # and that setting allow_missing after init still works
                est.allow_missing = False
                self.assertRaises(ValueError, est.fit, **data_dict)
                self.assertRaises(ValueError, est.dowhy.fit, **data_dict)

        for est in w_missing_models:
            with self.subTest(est=est, kind='missing W'):

                if 'Z' in inspect.getfullargspec(est.fit).kwonlyargs:
                    include_Z = True
                else:
                    include_Z = False

                data_dict = create_data_dict(y, T, X, X_missing, W, W_missing, Z,
                                             X_has_missing=False, W_has_missing=True, include_Z=include_Z)
                est.fit(**data_dict)
                est.effect(X)
                est_dowhy = est.dowhy.fit(**data_dict)

                # assert that we fail with a value error when we pass missing X to a model that doesn't support it
                data_dict_to_fail = create_data_dict(y, T, X, X_missing, W, W_missing, Z,
                                                     X_has_missing=True, W_has_missing=True, include_Z=include_Z)
                self.assertRaises(ValueError, est.fit, **data_dict_to_fail)
                self.assertRaises(ValueError, est.dowhy.fit, **data_dict_to_fail)

                # assert that fitting with missing values fails when allow_missing is False
                # and that setting allow_missing after init still works
                est.allow_missing = False
                self.assertRaises(ValueError, est.fit, **data_dict)
                self.assertRaises(ValueError, est.dowhy.fit, **data_dict)

        for est in metalearners:
            with self.subTest(est=est, kind='metalearner'):

                data_dict = create_data_dict(y, T, X, X_missing, W, W_missing, Z,
                                             X_has_missing=True, W_has_missing=False, include_Z=False)
                # metalearners don't support W
                data_dict.pop('W')

                # metalearners do support missing values in X
                est.fit(**data_dict)

                # dowhy never supports missing values in X
                self.assertRaises(ValueError, est.dowhy.fit, **data_dict)