separate one pager example as API quickstart from examples.rst

uber · Nov 7, 2023 · bef732d · bef732d
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diff --git a/docs/examples.rst b/docs/examples.rst
@@ -1,6 +1,8 @@
 Examples
 ========
 
+Working example notebooks are available in the `example folder <https://github.com/uber/causalml/tree/master/docs/examples>`_.
+
 .. toctree::
     :maxdepth: 1
 
@@ -23,310 +25,3 @@ Examples
     examples/uplift_tree_visualization
     examples/uplift_trees_with_synthetic_data
     examples/validation_with_tmle
-
-Working example notebooks are available in the `example folder <https://github.com/uber/causalml/tree/master/docs/examples>`_.
-
-Propensity Score
-----------------
-
-Propensity Score Estimation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-    from causalml.propensity import ElasticNetPropensityModel
-
-    pm = ElasticNetPropensityModel(n_fold=5, random_state=42)
-    ps = pm.fit_predict(X, y)
-
-Propensity Score Matching
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-    from causalml.match import NearestNeighborMatch, create_table_one
-
-    psm = NearestNeighborMatch(replace=False,
-                               ratio=1,
-                               random_state=42)
-    matched = psm.match_by_group(data=df,
-                                 treatment_col=treatment_col,
-                                 score_cols=score_cols,
-                                 groupby_col=groupby_col)
-
-    create_table_one(data=matched,
-                     treatment_col=treatment_col,
-                     features=covariates)
-
-Average Treatment Effect (ATE) Estimation
------------------------------------------
-
-Meta-learners and Uplift Trees
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In addition to the Methodology section, you can find examples in the links below for :ref:`Meta-Learner Algorithms` and :ref:`Tree-Based Algorithms`
-
-- Meta-learners (S/T/X/R): `meta_learners_with_synthetic_data.ipynb <https://github.com/uber/causalml/blob/master/examples/meta_learners_with_synthetic_data.ipynb>`_
-- Meta-learners (S/T/X/R) with multiple treatment: `meta_learners_with_synthetic_data_multiple_treatment.ipynb <https://github.com/uber/causalml/blob/master/examples/meta_learners_with_synthetic_data_multiple_treatment.ipynb>`_
-- Comparing meta-learners across simulation setups: `benchmark_simulation_studies.ipynb <https://github.com/uber/causalml/blob/master/examples/benchmark_simulation_studies.ipynb>`_
-- Doubly Robust (DR) learner: `dr_learner_with_synthetic_data.ipynb <https://github.com/uber/causalml/blob/master/examples/dr_learner_with_synthetic_data.ipynb>`_
-- TMLE learner: `validation_with_tmle.ipynb <https://github.com/uber/causalml/blob/master/examples/validation_with_tmle.ipynb>`_
-- Uplift Trees: `uplift_trees_with_synthetic_data.ipynb <https://github.com/uber/causalml/blob/master/examples/uplift_trees_with_synthetic_data.ipynb>`_
-
-.. code-block:: python
-
-    from causalml.inference.meta import LRSRegressor
-    from causalml.inference.meta import XGBTRegressor, MLPTRegressor
-    from causalml.inference.meta import BaseXRegressor
-    from causalml.inference.meta import BaseRRegressor
-    from xgboost import XGBRegressor
-    from causalml.dataset import synthetic_data
-
-    y, X, treatment, _, _, e = synthetic_data(mode=1, n=1000, p=5, sigma=1.0)
-
-    lr = LRSRegressor()
-    te, lb, ub = lr.estimate_ate(X, treatment, y)
-    print('Average Treatment Effect (Linear Regression): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))
-
-    xg = XGBTRegressor(random_state=42)
-    te, lb, ub = xg.estimate_ate(X, treatment, y)
-    print('Average Treatment Effect (XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))
-
-    nn = MLPTRegressor(hidden_layer_sizes=(10, 10),
-                     learning_rate_init=.1,
-                     early_stopping=True,
-                     random_state=42)
-    te, lb, ub = nn.estimate_ate(X, treatment, y)
-    print('Average Treatment Effect (Neural Network (MLP)): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))
-
-    xl = BaseXRegressor(learner=XGBRegressor(random_state=42))
-    te, lb, ub = xl.estimate_ate(X, treatment, y, e)
-    print('Average Treatment Effect (BaseXRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))
-
-    rl = BaseRRegressor(learner=XGBRegressor(random_state=42))
-    te, lb, ub =  rl.estimate_ate(X=X, p=e, treatment=treatment, y=y)
-    print('Average Treatment Effect (BaseRRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))
-
-
-More algorithms
-----------------
-
-Treatment optimization algorithms
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-We have developed :ref:`Counterfactual Unit Selection` and :ref:`Counterfactual Value Estimator` methods, please find the code snippet below and details in the following notebooks:
-
-- `counterfactual_unit_selection.ipynb <https://github.com/uber/causalml/blob/master/examples/counterfactual_unit_selection.ipynb>`_
-- `counterfactual_value_optimization.ipynb <https://github.com/uber/causalml/blob/master/examples/counterfactual_value_optimization.ipynb>`_
-
-.. code-block:: python
-
-    from causalml.optimize import CounterfactualValueEstimator
-    from causalml.optimize import get_treatment_costs, get_actual_value
-
-    # load data set and train test split
-    df_train, df_test = train_test_split(df)
-    train_idx = df_train.index
-    test_idx = df_test.index
-    # some more code here to initiate and train the Model, and produce tm_pred
-    # please refer to the counterfactual_value_optimization notebook for complete example
-
-    # run the counterfactual calculation with TwoModel prediction
-    cve = CounterfactualValueEstimator(treatment=df_test['treatment_group_key'],
-                                       control_name='control',
-                                       treatment_names=conditions[1:],
-                                       y_proba=y_proba,
-                                       cate=tm_pred,
-                                       value=conversion_value_array[test_idx],
-                                       conversion_cost=cc_array[test_idx],
-                                       impression_cost=ic_array[test_idx])
-
-    cve_best_idx = cve.predict_best()
-    cve_best = [conditions[idx] for idx in cve_best_idx]
-    actual_is_cve_best = df.loc[test_idx, 'treatment_group_key'] == cve_best
-    cve_value = actual_value.loc[test_idx][actual_is_cve_best].mean()
-
-    labels = [
-        'Random allocation',
-        'Best treatment',
-        'T-Learner',
-        'CounterfactualValueEstimator'
-    ]
-    values  = [
-        random_allocation_value,
-        best_ate_value,
-        tm_value,
-        cve_value
-    ]
-    # plot the result
-    plt.bar(labels, values)
-
-.. image:: ./_static/img/counterfactual_value_optimization.png
-    :width: 629
-
-Instrumental variables algorithms
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-- 2-Stage Least Squares (2SLS): `iv_nlsym_synthetic_data.ipynb <https://github.com/uber/causalml/blob/master/examples/iv_nlsym_synthetic_data.ipynb>`_
-
-
-Neural network based algorithms
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-- CEVAE: `cevae_example.ipynb <https://github.com/uber/causalml/blob/master/examples/cevae_example.ipynb>`_
-- DragonNet: `dragonnet_example.ipynb <https://github.com/uber/causalml/blob/master/examples/dragonnet_example.ipynb>`_
-
-
-Interpretation
-----------------
-Please see :ref:`Interpretable Causal ML` section
-
-Validation
-----------------
-
-Please see :ref:`validation` section
-
-
-Synthetic Data Generation Process
----------------------------------
-
-Single Simulation
-~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-  from causalml.dataset import *
-
-  # Generate synthetic data for single simulation
-  y, X, treatment, tau, b, e = synthetic_data(mode=1)
-  y, X, treatment, tau, b, e = simulate_nuisance_and_easy_treatment()
-
-  # Generate predictions for single simulation
-  single_sim_preds = get_synthetic_preds(simulate_nuisance_and_easy_treatment, n=1000)
-
-  # Generate multiple scatter plots to compare learner performance for a single simulation
-  scatter_plot_single_sim(single_sim_preds)
-
-  # Visualize distribution of learner predictions for a single simulation
-  distr_plot_single_sim(single_sim_preds, kind='kde')
-
-.. image:: ./_static/img/synthetic_dgp_scatter_plot.png
-    :width: 629
-
-
-Multiple Simulations
-~~~~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-  from causalml.dataset import *
-
-  # Generalize performance summary over k simulations
-  num_simulations = 12
-  preds_summary = get_synthetic_summary(simulate_nuisance_and_easy_treatment, n=1000, k=num_simulations)
-
-  # Generate scatter plot of performance summary
-  scatter_plot_summary(preds_summary, k=num_simulations)
-
-  # Generate bar plot of performance summary
-  bar_plot_summary(preds_summary, k=num_simulations)
-
-
-.. image:: ./_static/img/synthetic_dgp_scatter_plot_multiple.png
-    :width: 629
-
-.. image:: ./_static/img/synthetic_dgp_bar_plot_multiple.png
-    :width: 629
-
-Sensitivity Analysis
----------------------------
-
-For more details, please refer to the `sensitivity_example_with_synthetic_data.ipynb notebook <https://github.com/uber/causalml/blob/master/examples/sensitivity_example_with_synthetic_data.ipynb>`_.
-
-.. code-block:: python
-
-    from causalml.metrics.sensitivity import Sensitivity
-    from causalml.metrics.sensitivity import SensitivitySelectionBias
-    from causalml.inference.meta import BaseXLearner
-    from sklearn.linear_model import LinearRegression
-
-    # Calling the Base XLearner class and return the sensitivity analysis summary report
-    learner_x = BaseXLearner(LinearRegression())
-    sens_x = Sensitivity(df=df, inference_features=INFERENCE_FEATURES, p_col='pihat',
-                         treatment_col=TREATMENT_COL, outcome_col=OUTCOME_COL, learner=learner_x)
-    # Here for Selection Bias method will use default one-sided confounding function and alpha (quantile range of outcome values) input
-    sens_sumary_x = sens_x.sensitivity_analysis(methods=['Placebo Treatment',
-                                                         'Random Cause',
-                                                         'Subset Data',
-                                                         'Random Replace',
-                                                         'Selection Bias'], sample_size=0.5)
-
-    # Selection Bias: Alignment confounding Function
-    sens_x_bias_alignment = SensitivitySelectionBias(df, INFERENCE_FEATURES, p_col='pihat', treatment_col=TREATMENT_COL,
-                                                 outcome_col=OUTCOME_COL, learner=learner_x, confound='alignment',
-                                                 alpha_range=None)
-    # Plot the results by rsquare with partial r-square results by each individual features
-    sens_x_bias_alignment.plot(lls_x_bias_alignment, partial_rsqs_x_bias_alignment, type='r.squared', partial_rsqs=True)
-
-
-.. image:: ./_static/img/sensitivity_selection_bias_r2.png
-    :width: 629
-
-Feature Selection
----------------------------
-
-For more details, please refer to the `feature_selection.ipynb notebook <https://github.com/uber/causalml/blob/master/examples/feature_selection.ipynb>`_ and the associated paper reference by Zhao, Zhenyu, et al.
-
-.. code-block:: python
-
-    from causalml.feature_selection.filters import FilterSelect
-    from causalml.dataset import make_uplift_classification
-
-    # define parameters for simulation
-    y_name = 'conversion'
-    treatment_group_keys = ['control', 'treatment1']
-    n = 100000
-    n_classification_features = 50
-    n_classification_informative = 10
-    n_classification_repeated = 0
-    n_uplift_increase_dict = {'treatment1': 8}
-    n_uplift_decrease_dict = {'treatment1': 4}
-    delta_uplift_increase_dict = {'treatment1': 0.1}
-    delta_uplift_decrease_dict = {'treatment1': -0.1}
-
-    # make a synthetic uplift data set
-    random_seed = 20200808
-    df, X_names = make_uplift_classification(
-        treatment_name=treatment_group_keys,
-        y_name=y_name,
-        n_samples=n,
-        n_classification_features=n_classification_features,
-        n_classification_informative=n_classification_informative,
-        n_classification_repeated=n_classification_repeated,
-        n_uplift_increase_dict=n_uplift_increase_dict,
-        n_uplift_decrease_dict=n_uplift_decrease_dict,
-        delta_uplift_increase_dict = delta_uplift_increase_dict,
-        delta_uplift_decrease_dict = delta_uplift_decrease_dict,
-        random_seed=random_seed
-    )
-
-    # Feature selection with Filter method
-    filter_f = FilterSelect()
-    method = 'F'
-    f_imp = filter_f.get_importance(df, X_names, y_name, method,
-                          treatment_group = 'treatment1')
-    print(f_imp)
-
-    # Use likelihood ratio test method
-    method = 'LR'
-    lr_imp = filter_f.get_importance(df, X_names, y_name, method,
-                          treatment_group = 'treatment1')
-    print(lr_imp)
-
-    # Use KL divergence method
-    method = 'KL'
-    kl_imp = filter_f.get_importance(df, X_names, y_name, method,
-                          treatment_group = 'treatment1',
-                          n_bins=10)
-    print(kl_imp)
-
diff --git a/docs/examples/dragonnet_example.ipynb b/docs/examples/dragonnet_example.ipynb
@@ -97,7 +97,7 @@
     }
    },
    "source": [
-    "# IHDP semi-synthetic dataset\n",
+    "## IHDP semi-synthetic dataset\n",
     "\n",
     "Hill introduced a semi-synthetic dataset constructed from the Infant Health\n",
     "and Development Program (IHDP). This dataset is based on a randomized experiment\n",
@@ -908,7 +908,7 @@
     }
    },
    "source": [
-    "# `causalml` Synthetic Data Generation Method"
+    "## Synthetic Data Generation"
    ]
   },
   {

diff --git a/docs/index.rst b/docs/index.rst
@@ -9,6 +9,7 @@ Contents:
     about
     methodology
     installation
+    quickstart
     examples
     interpretation
     validation