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meta.yaml
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meta.yaml
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{% set name = "causalml" %}
{% set version = "0.15.1" %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/causalml-{{ version }}.tar.gz
sha256: 99f3cf70cdeb19c6a043c24fe555fc8d1e60d8df8f4567ee6aaf56bc85fc2165
build:
number: 1
# pytorch doesn't provide a windows build
skip: true # [win]
script: {{ PYTHON }} -m pip install . -vv
requirements:
build:
- {{ compiler('c') }}
- {{ stdlib("c") }}
- {{ compiler('cxx') }}
- python # [build_platform != target_platform]
- cross-python_{{ target_platform }} # [build_platform != target_platform]
- cython <=0.29.34 # [build_platform != target_platform]
- numpy # [build_platform != target_platform]
host:
- pip
- python
- cython <=0.29.34
- setuptools >=41.0.0
- numpy
- scikit-learn >=0.22
run:
- python
- setuptools >=41.0.0
- {{ pin_compatible('numpy') }}
- forestci ==0.6
- pathos ==0.2.9
- scipy >=1.4.1
- matplotlib-base
- pandas >=0.24.1
- scikit-learn >=0.22
- statsmodels >=0.9.0
- cython <=0.29.34
- seaborn-base
- xgboost
- pydotplus
- tqdm
- shap
- dill
- lightgbm
- pygam
- packaging
- pytorch
- pyro-ppl
- python-graphviz
test:
imports:
- causalml
- causalml.inference.meta
commands:
- pip check
requires:
- pip
about:
home: https://causalml.readthedocs.io/en/latest/?badge=latest
summary: Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms.
license: Apache-2.0
license_file: LICENSE
description: |
<a href="https://github.com/uber/causalml">
<img src="https://github.com/raw/uber/causalml/master/docs/_static/img/logo/causalml_logo.png" alt="CausalML banner" />
</a>
``Causal ML`` is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research.
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.
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
extra:
recipe-maintainers:
- jeongyoonlee
- ppstacy
- pavelzw