reslove coments
@@ -12,7 +12,7 @@ source:
build:
number: 0
- noarch: python
+ skip: true # [win]
script: {{ PYTHON }} -m pip install . -vv
requirements:
@@ -22,9 +22,9 @@ requirements:
- pip
- pytest
- python >=3.6
- - cython ==0.29.23
+ - cython
- setuptools >=41.0.0
- - numpy >=1.18.5
+ - numpy
- scikit-learn >=0.22.0
- python-graphviz
- graphviz
@@ -32,7 +32,7 @@ requirements:
- python >=3.6
- setuptools >=41.0.0
- pip >=10.0
- - numpy >=1.18.5
+ - {{ pin_compatibile('numpy') }}
- scipy >=1.4.1
- matplotlib-base
- pandas >=0.24.1
@@ -76,33 +76,6 @@ about:
It provides a standard interface that allows user to estimate the **Conditional Average Treatment Effect** (CATE) or **Individual Treatment Effect** (ITE) from experimental or observational data.
Essentially, it estimates the causal impact of intervention **T** on outcome **Y** for users with observed features **X**, without strong assumptions on the model form.
- Typical use cases include:
-
- - **Campaign Targeting Optimization**: An important lever to increase ROI in an advertising campaign is to target the ad to the set of customers who will have a favorable response in a given KPI such as engagement or sales. CATE identifies these customers by estimating the effect of the KPI from ad exposure at the individual level from A/B experiment or historical observational data.
- - **Personalized Engagement**: Company has multiple options to interact with its customers such as different product choices in up-sell or messaging channels for communications. One can use CATE to estimate the heterogeneous treatment effect for each customer and treatment option combination for an optimal personalized recommendation system.
-
- The package currently supports the following methods:
-
- - Tree-based algorithms
- - :ref:`Uplift Random Forests <Uplift Tree>` on KL divergence, Euclidean Distance, and Chi-Square
- - :ref:`Uplift Random Forests <Uplift Tree>` on Contextual Treatment Selection
- - :ref:`Uplift Random Forests <Uplift Tree>` on delta-delta-p (:math:`\Delta\Delta P`) criterion (only for binary trees and two-class problems)
- - Meta-learner algorithms
- - :ref:`S-learner`
- - :ref:`T-learner`
- - :ref:`X-learner`
- - :ref:`R-learner`
- - :ref:`Doubly Robust (DR) learner`
- - Instrumental variables algorithms
- - :ref:`2-Stage Least Squares (2SLS)`
- - :ref:`Doubly Robust Instrumental Variable (DRIV) learner`
- - Neural network based algorithms
- - CEVAE
- - DragonNet
- - Treatment optimization algorithms
- - :ref:`Counterfactual Unit Selection`
- - :ref:`Counterfactual Value Estimator`
-
PyPI: [(https://pypi.org/project/causalml/]((https://pypi.org/project/causalml/)
doc_url: http://causalml.readthedocs.io/en/latest/?badge=latest
dev_url: https://github.com/uber/causalml
