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Merge pull request #346 from DoubleML/s-add-pre-commit-ci
Update workflows
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‎.github/workflows/pytest.yml‎

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- uses: actions/checkout@v4
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with:
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fetch-depth: 2
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- name: Set up Python ${{ matrix.config.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.config.python-version }}
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- name: Install OpenMP runtime for unit tests with xgboost learners
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if: matrix.config.os == 'macOS-latest'
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run: brew install libomp
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -e .[dev,rdd]
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- name: Lint with ruff
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run: |
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# stop the build if there are Python syntax errors or undefined names
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ruff check . --select E9,F63,F7,F82 --output-format=full --statistics
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# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
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ruff check . --exit-zero --line-length 127 --output-format=full --statistics
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- name: Check code formatting with black
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- name: Run pre-commit checks
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run: |
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black --check --verbose . || echo "Warning: Code is not formatted according to black"
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exit 0
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pip install pre-commit
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pre-commit run --all-files --show-diff-on-failure
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- name: Test with pytest
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if: |
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matrix.config.os != 'ubuntu-latest' ||

‎.pre-commit-config.yaml‎

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hooks:
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- id: black
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.11.7
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rev: v0.12.3
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hooks:
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- id: ruff
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args: ["--fix", "--output-format=full"]

‎doubleml/did/did_cs_binary.py‎

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class DoubleMLDIDCSBinary(LinearScoreMixin, DoubleML):
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"""Double machine learning for difference-in-differences models with repeated cross sections (binary setting in terms of group and time
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combinations).
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"""Double machine learning for difference-in-differences models with repeated cross sections
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(binary setting in terms of group and time combinations).
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Parameters
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----------
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obj_dml_data : :class:`DoubleMLPanelData` object
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The :class:`DoubleMLPanelData` object providing the data and specifying the variables for the causal model.
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Parameters
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----------
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obj_dml_data : :class:`DoubleMLPanelData` object
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The :class:`DoubleMLPanelData` object providing the data and specifying the variables for the causal model.
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g_value : int
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The value indicating the treatment group (first period with treatment).
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Default is ``None``. This implements the case for the smallest, non-zero value of G.
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g_value : int
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The value indicating the treatment group (first period with treatment).
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Default is ``None``. This implements the case for the smallest, non-zero value of G.
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t_value_pre : int
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The value indicating the baseline pre-treatment period.
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t_value_pre : int
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The value indicating the baseline pre-treatment period.
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t_value_eval : int
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The value indicating the period for evaluation.
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t_value_eval : int
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The value indicating the period for evaluation.
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ml_g : estimator implementing ``fit()`` and ``predict()``
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A machine learner implementing ``fit()`` and ``predict()`` methods (e.g.
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:py:class:`sklearn.ensemble.RandomForestRegressor`) for the nuisance function :math:`g_0(d,X) = E[Y_1-Y_0|D=d, X]`.
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For a binary outcome variable :math:`Y` (with values 0 and 1), a classifier implementing ``fit()`` and
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``predict_proba()`` can also be specified. If :py:func:`sklearn.base.is_classifier` returns ``True``,
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``predict_proba()`` is used otherwise ``predict()``.
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ml_g : estimator implementing ``fit()`` and ``predict()``
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A machine learner implementing ``fit()`` and ``predict()`` methods (e.g.
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:py:class:`sklearn.ensemble.RandomForestRegressor`) for the nuisance function :math:`g_0(d,X) = E[Y_1-Y_0|D=d, X]`.
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For a binary outcome variable :math:`Y` (with values 0 and 1), a classifier implementing ``fit()`` and
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``predict_proba()`` can also be specified. If :py:func:`sklearn.base.is_classifier` returns ``True``,
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``predict_proba()`` is used otherwise ``predict()``.
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ml_m : classifier implementing ``fit()`` and ``predict_proba()``
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A machine learner implementing ``fit()`` and ``predict_proba()`` methods (e.g.
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:py:class:`sklearn.ensemble.RandomForestClassifier`) for the nuisance function :math:`m_0(X) = E[D=1|X]`.
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Only relevant for ``score='observational'``.
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ml_m : classifier implementing ``fit()`` and ``predict_proba()``
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A machine learner implementing ``fit()`` and ``predict_proba()`` methods (e.g.
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:py:class:`sklearn.ensemble.RandomForestClassifier`) for the nuisance function :math:`m_0(X) = E[D=1|X]`.
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Only relevant for ``score='observational'``.
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control_group : str
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Specifies the control group. Either ``'never_treated'`` or ``'not_yet_treated'``.
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Default is ``'never_treated'``.
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control_group : str
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Specifies the control group. Either ``'never_treated'`` or ``'not_yet_treated'``.
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Default is ``'never_treated'``.
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anticipation_periods : int
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Number of anticipation periods. Default is ``0``.
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anticipation_periods : int
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Number of anticipation periods. Default is ``0``.
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n_folds : int
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Number of folds.
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Default is ``5``.
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n_folds : int
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Number of folds.
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Default is ``5``.
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n_rep : int
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Number of repetitions for the sample splitting.
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Default is ``1``.
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n_rep : int
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Number of repetitions for the sample splitting.
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Default is ``1``.
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score : str
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A str (``'observational'`` or ``'experimental'``) specifying the score function.
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The ``'experimental'`` scores refers to an A/B setting, where the treatment is independent
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from the pretreatment covariates.
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Default is ``'observational'``.
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score : str
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A str (``'observational'`` or ``'experimental'``) specifying the score function.
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The ``'experimental'`` scores refers to an A/B setting, where the treatment is independent
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from the pretreatment covariates.
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Default is ``'observational'``.
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in_sample_normalization : bool
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Indicates whether to use a slightly different normalization from Sant'Anna and Zhao (2020).
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Default is ``True``.
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in_sample_normalization : bool
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Indicates whether to use a slightly different normalization from Sant'Anna and Zhao (2020).
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Default is ``True``.
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trimming_rule : str
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A str (``'truncate'`` is the only choice) specifying the trimming approach.
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Default is ``'truncate'``.
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trimming_rule : str
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A str (``'truncate'`` is the only choice) specifying the trimming approach.
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Default is ``'truncate'``.
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trimming_threshold : float
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The threshold used for trimming.
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Default is ``1e-2``.
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trimming_threshold : float
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The threshold used for trimming.
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Default is ``1e-2``.
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draw_sample_splitting : bool
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Indicates whether the sample splitting should be drawn during initialization of the object.
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Default is ``True``.
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draw_sample_splitting : bool
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Indicates whether the sample splitting should be drawn during initialization of the object.
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Default is ``True``.
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print_periods : bool
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Indicates whether to print information about the evaluated periods.
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Default is ``False``.
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print_periods : bool
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Indicates whether to print information about the evaluated periods.
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Default is ``False``.
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"""
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"""
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def __init__(
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self,

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