Merge pull request #26 from CyberAgentAILab/doc/multi-task

Add a guide for multi-task learning

Author: TomeHirata

README.md

@@ -1,6 +1,6 @@
 ## Overview
 
-This a Python package for building the regression adjusted distribution function estimator proposed in "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction". For the details of this package, see [the documentation](https://cyberagentailab.github.io/python-dte-adjustment/).
+`dte_adj` is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils. For the details of this package, see [the documentation](https://cyberagentailab.github.io/python-dte-adjustment/).
 
 ## Installation
 

docs/source/get_started.rst

@@ -81,7 +81,7 @@ A convenience function is available to visualize distribution effects. This meth
    :align: center
 
 To initialize the adjusted distribution function, the base model for conditional distribution function needs to be passed.
-In the following example, we use Logistic Regression. Please make sure that your base model implements `fit` and `predict_proba` methods.
+In the following example, we use Logistic Regression. Please make sure that your base model implements ``fit`` and ``predict_proba`` methods.
 
 .. code-block:: python
 
@@ -117,7 +117,7 @@ Confidence bands can be computed in different ways. In the following code, we us
    :width: 450px
    :align: center
 
-Also, an uniform confidence band is used when "uniform" is specified for the "variance_type" argument.
+Also, an uniform confidence band is used when ``uniform`` is specified for the ``variance_type`` argument.
 
 .. code-block:: python
 
@@ -130,7 +130,7 @@ Also, an uniform confidence band is used when "uniform" is specified for the "va
    :width: 450px
    :align: center
 
-To compute PTE, we can use "predict_pte" method.
+To compute PTE, we can use ``predict_pte`` method.
 
 .. code-block:: python
 
@@ -143,7 +143,7 @@ To compute PTE, we can use "predict_pte" method.
    :width: 450px
    :align: center
 
-To compute QTE, we use "predict_qte" method. The confidence band is computed by bootstrap method.
+To compute QTE, we use ``predict_qte`` method. The confidence band is computed by bootstrap method.
 
 .. code-block:: python
 
@@ -157,11 +157,23 @@ To compute QTE, we use "predict_qte" method. The confidence band is computed by
    :width: 450px
    :align: center
 
-You can use any model with "predict_proba" or "predict" method to adjust the distribution function estimation. For example, the following code use XGBoost classifier to estimate the conditional distribution.
+You can use any model with ``predict_proba`` or ``predict`` method to adjust the distribution function estimation. 
+For example, the following code use XGBoost classifier to estimate the conditional distribution.
 
 .. code-block:: python
 
   import xgboost as xgb
   estimator = dte_adj.AdjustedDistributionEstimator(xgb.XGBClassifier(), folds=3)
   estimator.fit(X, D, Y)
-  cdf = estimator.predict(1, locations)
+  cdf = estimator.predict(1, locations)
+
+``predict_dte`` and ``predict_pte`` methods provide an option to train a model for multiple locations simultaneously.
+To enable the feature, pass ``is_multi_task=True``.
+
+.. code-block:: python
+
+  from sklearn.linear_model import LinearRegression
+  model = LinearRegression()
+  estimator = dte_adj.AdjustedDistributionEstimator(model, folds=3)
+  estimator.fit(X, D, Y)
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, is_multi_task=True, locations=locations, variance_type="moment")