econml/data/dynamic_panel_dgp.py [211:253]:
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class AbstracDynamicPanelDGP:

    def __init__(self, n_periods, n_treatments, n_x):
        self.n_periods = n_periods
        self.n_treatments = n_treatments
        self.n_x = n_x
        return

    def create_instance(self, *args, **kwargs):
        pass

    def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123):
        pass

    def static_policy_data(self, n_units, tau, random_seed=123):
        def policy_gen(Tpre, X, period):
            return tau[period]
        return self._gen_data_with_policy(n_units, policy_gen, random_seed=random_seed)

    def adaptive_policy_data(self, n_units, policy_gen, random_seed=123):
        return self._gen_data_with_policy(n_units, policy_gen, random_seed=random_seed)

    def static_policy_effect(self, tau, mc_samples=1000):
        Y_tau, _, _, _ = self.static_policy_data(mc_samples, tau)
        Y_zero, _, _, _ = self.static_policy_data(
            mc_samples, np.zeros((self.n_periods, self.n_treatments)))
        return np.mean(Y_tau[np.arange(Y_tau.shape[0]) % self.n_periods == self.n_periods - 1]) - \
            np.mean(Y_zero[np.arange(Y_zero.shape[0]) %
                           self.n_periods == self.n_periods - 1])

    def adaptive_policy_effect(self, policy_gen, mc_samples=1000):
        Y_tau, _, _, _ = self.adaptive_policy_data(mc_samples, policy_gen)
        Y_zero, _, _, _ = self.static_policy_data(
            mc_samples, np.zeros((self.n_periods, self.n_treatments)))
        return np.mean(Y_tau[np.arange(Y_tau.shape[0]) % self.n_periods == self.n_periods - 1]) - \
            np.mean(Y_zero[np.arange(Y_zero.shape[0]) %
                           self.n_periods == self.n_periods - 1])


class DynamicPanelDGP(AbstracDynamicPanelDGP):

    def __init__(self, n_periods, n_treatments, n_x):
        super().__init__(n_periods, n_treatments, n_x)
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prototypes/dynamic_dml/dynamic_panel_dgp.py [6:48]:
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class AbstracDynamicPanelDGP:

    def __init__(self, n_periods, n_treatments, n_x):
        self.n_periods = n_periods
        self.n_treatments = n_treatments
        self.n_x = n_x
        return

    def create_instance(self, *args, **kwargs):
        pass

    def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123):
        pass

    def static_policy_data(self, n_units, tau, random_seed=123):
        def policy_gen(Tpre, X, period):
            return tau[period]
        return self._gen_data_with_policy(n_units, policy_gen, random_seed=random_seed)

    def adaptive_policy_data(self, n_units, policy_gen, random_seed=123):
        return self._gen_data_with_policy(n_units, policy_gen, random_seed=random_seed)

    def static_policy_effect(self, tau, mc_samples=1000):
        Y_tau, _, _, _ = self.static_policy_data(mc_samples, tau)
        Y_zero, _, _, _ = self.static_policy_data(
            mc_samples, np.zeros((self.n_periods, self.n_treatments)))
        return np.mean(Y_tau[np.arange(Y_tau.shape[0]) % self.n_periods == self.n_periods - 1]) - \
            np.mean(Y_zero[np.arange(Y_zero.shape[0]) %
                           self.n_periods == self.n_periods - 1])

    def adaptive_policy_effect(self, policy_gen, mc_samples=1000):
        Y_tau, _, _, _ = self.adaptive_policy_data(mc_samples, policy_gen)
        Y_zero, _, _, _ = self.static_policy_data(
            mc_samples, np.zeros((self.n_periods, self.n_treatments)))
        return np.mean(Y_tau[np.arange(Y_tau.shape[0]) % self.n_periods == self.n_periods - 1]) - \
            np.mean(Y_zero[np.arange(Y_zero.shape[0]) %
                           self.n_periods == self.n_periods - 1])


class DynamicPanelDGP(AbstracDynamicPanelDGP):

    def __init__(self, n_periods, n_treatments, n_x):
        super().__init__(n_periods, n_treatments, n_x)
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