This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML
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 [1]. 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: A 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
Uplift tree/random forests on KL divergence, Euclidean Distance, and Chi-Square [2]
Uplift tree/random forests on Contextual Treatment Selection [3]
DragonNet [11] - with causalml[tf] installation (see Installation)
Installation
Installation with conda is recommended. conda environment files for Python 3.6, 3.7, 3.8 and 3.9 are available in the repository. To use models under the inference.tf module (e.g. DragonNet), additional dependency of tensorflow is required. For detailed instructions, see below.
Install using conda:
Install from conda-forge
Directly install from the conda-forge channel using conda.
$ conda install -c conda-forge causalml
Install with the conda virtual environment
This will create a new conda virtual environment named causalml-[tf-]py3x, where x is in [6, 7, 8, 9]. e.g. causalml-py37 or causalml-tf-py38. If you want to change the name of the environment, update the relevant YAML file in envs/
$ git clone https://github.com/uber/causalml.git
$ cd causalml/envs/
$ conda env create -f environment-py38.yml # for the virtual environment with Python 3.8 and CausalML
$ conda activate causalml-py38
(causalml-py38)
Install causalml with tensorflow
$ git clone https://github.com/uber/causalml.git
$ cd causalml/envs/
$ conda env create -f environment-tf-py38.yml # for the virtual environment with Python 3.8 and CausalML
$ conda activate causalml-tf-py38
(causalml-tf-py38) pip install -U numpy # this step is necessary to fix [#338](https://github.com/uber/causalml/issues/338)
(Talk) Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber at KDD 2021 Tutorials (website and slide links)
@misc{chen2020causalml,
title={CausalML: Python Package for Causal Machine Learning},
author={Huigang Chen and Totte Harinen and Jeong-Yoon Lee and Mike Yung and Zhenyu Zhao},
year={2020},
eprint={2002.11631},
archivePrefix={arXiv},
primaryClass={cs.CY}
}
Literature
Chen, Huigang, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).
Radcliffe, Nicholas J., and Patrick D. Surry. "Real-world uplift modelling with significance-based uplift trees." White Paper TR-2011-1, Stochastic Solutions (2011): 1-33.
Zhao, Yan, Xiao Fang, and David Simchi-Levi. "Uplift modeling with multiple treatments and general response types." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.
Athey, Susan, and Guido Imbens. "Recursive partitioning for heterogeneous causal effects." Proceedings of the National Academy of Sciences 113.27 (2016): 7353-7360.
Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165.
Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017).
Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61.4 (2005): 962-973.
Van Der Laan, Mark J., and Daniel Rubin. "Targeted maximum likelihood learning." The international journal of biostatistics 2.1 (2006).
Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020).
Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017).
Shi, Claudia, David M. Blei, and Victor Veitch. "Adapting neural networks for the estimation of treatment effects." 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019.
Zhao, Zhenyu, Yumin Zhang, Totte Harinen, and Mike Yung. "Feature Selection Methods for Uplift Modeling." arXiv preprint arXiv:2005.03447 (2020).
Zhao, Zhenyu, and Totte Harinen. "Uplift modeling for multiple treatments with cost optimization." In 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 422-431. IEEE, 2019.
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