Metadata-Version: 2.1
Name: mcf
Version: 0.7.1
Summary: The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.
Home-page: https://mcfpy.github.io/mcf/#/
Author: mlechner
Author-email: michael.lechner@unisg.ch
License: MIT
Keywords: causal machine learning, heterogeneous treatment effects, causal forests, optimal policy learning
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.12
License-File: LICENSE
Requires-Dist: ray>=2.36.0
Requires-Dist: pandas>=2.2.2
Requires-Dist: matplotlib>=3.9.2
Requires-Dist: numba>=0.60.0
Requires-Dist: sympy>=1.13.3
Requires-Dist: scikit-learn>=1.5.2
Requires-Dist: scipy>=1.14.1
Requires-Dist: torch>=2.4.1
Requires-Dist: fpdf2>=2.7.9

The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.
