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Added blb inference option to the OrthoForest #214

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Feb 7, 2020
Merged

Added blb inference option to the OrthoForest #214

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8229674
Added `blb` inference option to the OrthoForest
Jan 28, 2020
e704e29
Addressed PR comments
Feb 7, 2020
3dd9329
Merge branch 'master' into moprescu/orf_inference
Feb 7, 2020
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5 changes: 2 additions & 3 deletions doc/spec/estimation/forest.rst
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Expand Up @@ -363,8 +363,7 @@ and the `ForestLearners Jupyter notebook <https://github.com/microsoft/EconML/bl
>>> est.fit(Y, T, W, W)
<econml.ortho_forest.ContinuousTreatmentOrthoForest object at 0x...>
>>> print(est.effect(W[:2]))
[[1. ]
[1.2]]
[1.00... 1.19...]
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If I understand correctly, the shape changing here is good because it was wrong before.

Should I be concerned that the values themselves have changed as well?

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Yes, the shape change is intentional. The values have changed because the trees are partitioned into segments ("little bags") that share data, so the same random seed gives slightly different results now.

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Hm, so the trees are partitioned even when fit's inference option is None rather than 'blb'?

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Yes, otherwise we'd have two ways of partitioning the tress and it would be hard to keep track of. It doesn't affect the estimate much since that's done using all of the 2*n_trees leaves. The only difference is which data samples a particular tree uses, i.e. groups slice_len trees subsample from the same n_samples/2 samples but which n_samples/2 are used differs from group to group.


Similarly, we can call :class:`.DiscreteTreatmentOrthoForest`:

Expand All @@ -377,7 +376,7 @@ Similarly, we can call :class:`.DiscreteTreatmentOrthoForest`:
>>> est.fit(Y, T, W, W)
<econml.ortho_forest.DiscreteTreatmentOrthoForest object at 0x...>
>>> print(est.effect(W[:2]))
[1. 1.2]
[1.01... 1.25...]

Let's now look at a more involved example with a high-dimensional set of confounders :math:`W`
and with more realistic noisy data. In this case we can just use the default parameters
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21 changes: 21 additions & 0 deletions doc/spec/inference.rst
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Expand Up @@ -136,6 +136,27 @@ and the :class:`.ForestDRLearner`. You can enable such intervals by setting ``in
This inference is enabled by our implementation of the :class:`.SubsampledHonestForest` extension to the scikit-learn
:class:`~sklearn.ensemble.RandomForestRegressor`.


OrthoForest Bootstrap of Little Bags Inference
==============================================

For the Orthogonal Random Forest estimators (see :class:`.ContinuousTreatmentOrthoForest`, :class:`.DiscreteTreatmentOrthoForest`),
we provide confidence intervals built via the bootstrap-of-little-bags approach ([Athey2019]_). This technique is well suited for
estimating the uncertainty of the honest causal forests underlying the OrthoForest estimators. You can enable such intervals by setting
``inference='blb'``, e.g.:

.. testcode::

from econml.ortho_forest import ContinuousTreatmentOrthoForest
from econml.sklearn_extensions.linear_model import WeightedLasso
est = ContinuousTreatmentOrthoForest(n_trees=10,
min_leaf_size=3,
model_T=WeightedLasso(alpha=0.01),
model_Y=WeightedLasso(alpha=0.01))
est.fit(y, t, X, W, inference='blb')
point = est.const_marginal_effect(X)
lb, ub = est.const_marginal_effect_interval(X, alpha=0.05)

.. todo::
* Subsampling
* Doubly Robust Gradient Inference
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