Remove apply_crossfitting and dml_procedure options from DoubleML class
#227
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| @@ -134,6 +120,11 @@ | |||
| y_treat = self._dml_data.y[self._dml_data.d == self.treatment] | |||
| self._coef_start_val = np.mean(y_treat[y_treat >= np.quantile(y_treat, self.quantile)]) | |||
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| # set stratication for resampling | |||
| self._strata = self._dml_data.d | |||
Check warning
Code scanning / CodeQL
Overwriting attribute in super-class or sub-class
| @@ -153,6 +145,13 @@ | |||
| self._coef_bounds = (self._dml_data.y.min(), self._dml_data.y.max()) | |||
| self._coef_start_val = np.quantile(self._dml_data.y[self._dml_data.d == self.treatment], self.quantile) | |||
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| # set stratication for resampling | |||
| self._strata = self._dml_data.d.reshape(-1, 1) + 2 * self._dml_data.z.reshape(-1, 1) | |||
Check warning
Code scanning / CodeQL
Overwriting attribute in super-class or sub-class
| if draw_sample_splitting: | ||
| self.draw_sample_splitting() | ||
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| self._external_predictions_implemented = True |
Check warning
Code scanning / CodeQL
Overwriting attribute in super-class or sub-class
| @@ -165,6 +152,13 @@ | |||
| self._coef_bounds = (self._dml_data.y.min(), self._dml_data.y.max()) | |||
| self._coef_start_val = np.quantile(self._dml_data.y[self._dml_data.d == self.treatment], self.quantile) | |||
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| # set stratication for resampling | |||
| self._strata = self._dml_data.d | |||
Check warning
Code scanning / CodeQL
Overwriting attribute in super-class or sub-class
| if draw_sample_splitting: | ||
| self.draw_sample_splitting() | ||
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| self._external_predictions_implemented = True |
Check warning
Code scanning / CodeQL
Overwriting attribute in super-class or sub-class
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Comments
Remove the options
apply_crossfittinganddml_procedure.dml1is not used and crossfitting should generally be done (if not wanted the predictions can still be generated externally)Further, removed the
boot_coefattribute as the name is slightly misleading and it is not required for bootstrapped confidence intervals.Simplify the
fit()implementation.PR Checklist