mcf 0.10.0

Modified Causal Forest in Python

mcf – Modified Causal Forest in Python

mcf is a Python package implementing the Modified Causal Forest (MCF) methodology introduced by Lechner (2018) for estimating heterogeneous causal effects.

It provides a flexible framework for causal machine learning with support for binary and multiple treatments in both experimental and observational data settings.

In addition to treatment effect estimation, mcf enables data-driven policy learning through optimal treatment allocation rules based on estimated potential outcomes.

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Documentation and Maintenance

• Documentation and website: https://mcfpy.github.io/mcf/#/
• Bug tracker: https://github.com/mcfpy/mcf/issues

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Main Features

The mcf package provides two core components for causal machine learning:

Modified Causal Forest

The ModifiedCausalForest class implements a flexible tree-based framework for estimating heterogeneous treatment effects. It supports:

• Estimation of and inference for individualized treatment effects (IATEs) and aggregates as the (group) average treatment effects (GATEs, ATEs)
• Binary and multiple treatment settings
• Experimental and observational data
• Flexible covariate specification (ordered and unordered variables)

The object-oriented design provides a unified workflow for model training, prediction, and extraction of causal estimates.

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Optimal Policy Learning

The OptimalPolicy class enables data-driven treatment assignment by learning decision rules that maximize a reward function. It provides:

• Learning of optimal treatment allocation rules
• Policy evaluation on training and test data
• Support for multiple treatment alternatives and policy constraints

This allows translation of policy scores into actionable decision rules for optimal policy design.

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Citation

The implementation builds on the following literature:

• Lechner (2019): Modified Causal Forests for Estimating Heterogeneous Causal Effects. arXiv:1812.09487.

@misc{lechner2019modifiedcausalforest,
  title        = {Modified Causal Forests for Estimating Heterogeneous Causal Effects},
  author       = {Michael Lechner},
  year         = {2019},
  eprint       = {1812.09487},
  archivePrefix = {arXiv},
  primaryClass = {econ.EM},
  url          = {https://arxiv.org/abs/1812.09487}
}

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License

This project is distributed under the MIT License.