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TomeHirata merged 2 commits into from
Jul 11, 2025
Merged

Refactor predict_pte #43

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7eabd9b
remove width from predict_pte
TomeHirata Jul 11, 2025
89bbf73
simplify
TomeHirata Jul 11, 2025
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14 changes: 8 additions & 6 deletions docs/source/get_started.rst
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Expand Up @@ -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, Logistic Regression is used. Please make sure that your base model implements ``fit`` and ``predict_proba`` methods.

.. code-block:: python

Expand All @@ -104,7 +104,7 @@ DTE can be computed and visualized in the following code.
:width: 450px
:align: center

Confidence bands can be computed in different ways. In the following code, we use moment method to calculate the confidence band.
Confidence bands can be computed in different ways. In the following code, moment condition is used to calculate the confidence band.

.. code-block:: python

Expand All @@ -130,20 +130,22 @@ Also, an uniform confidence band is used when ``uniform`` is specified for the `
:width: 450px
:align: center

To compute PTE, we can use ``predict_pte`` method.
To compute PTE, you can use ``predict_pte`` method. The ``locations`` parameter defines interval boundaries, and the method returns probability treatment effects for each interval.
For each interval, the starting point is not included but the ending point is included. For example, if the `locations` is [0, 1, 2], PTE is computed for `(0, 1]` and `(1, 2]`.

.. code-block:: python

pte, lower_bound, upper_bound = estimator.predict_pte(target_treatment_arm=1, control_treatment_arm=0, width=1, locations=locations, variance_type="simple")
plot(locations, pte, lower_bound, upper_bound, chart_type="bar", title="PTE of adjusted estimator with simple confidence band")
pte, lower_bound, upper_bound = estimator.predict_pte(target_treatment_arm=1, control_treatment_arm=0, locations=locations, variance_type="simple")
# Note: pte will have shape (len(locations)-1,) since it computes intervals between locations
plot(locations[:-1], pte, lower_bound, upper_bound, chart_type="bar", title="PTE of adjusted estimator with simple confidence band")

.. image:: _static/pte_empirical.png
:alt: PTE of adjusted estimator with simple confidence band
:height: 300px
:width: 450px
:align: center

To compute QTE, we use ``predict_qte`` method. The confidence band is computed by bootstrap method.
To compute QTE, you can use ``predict_qte`` method. The confidence band is computed by bootstrap method.

.. code-block:: python

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