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Uplift modeling and causal inference with machine learning algorithms
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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. 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
    • Uplift tree/random forests on Contextual Treatment Selection
  • Meta-learner algorithms
    • S-learner
    • T-learner
    • X-learner
    • R-learner

Installation

Prerequisites

Install dependencies:

$ pip install -r requirements.txt

Install from pip:

$ pip install causalml

Install from source:

$ git clone https://github.com/uber/causalml.git
$ cd causalml
$ python setup.py build_ext --inplace
$ python setup.py install

Quick Start

Average Treatment Effect Estimation with S, T, and X Learners

from causalml.inference.meta import LRSRegressor
from causalml.inference.meta import XGBTRegressor, MLPTRegressor
from causalml.inference.meta import BaseXRegressor
from causalml.dataset import synthetic_data

y, X, treatment, _, _, e = synthetic_data(mode=1, n=1000, p=5, sigma=1.0)

lr = LRSRegressor()
te, lb, ub = lr.estimate_ate(X, treatment, y)
logger.info('Average Treatment Effect (Linear Regression): {:.2f} ({:.2f}, {:.2f})'.format(te, lb, ub))

xg = XGBTRegressor(random_state=42)
te, lb, ub = xg.estimate_ate(X, treatment, y)
logger.info('Average Treatment Effect (XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te, lb, ub))

nn = MLPTRegressor(hidden_layer_sizes=(10, 10),
                 learning_rate_init=.1,
                 early_stopping=True,
                 random_state=42)
te, lb, ub = nn.estimate_ate(X, treatment, y)
logger.info('Average Treatment Effect (Neural Network (MLP)): {:.2f} ({:.2f}, {:.2f})'.format(te, lb, ub))

xl = BaseXRegressor(learner=XGBRegressor(random_state=42))
te, lb, ub = xl.estimate_ate(X, e, treatment, y)
logger.info('Average Treatment Effect (XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te, lb, ub))

Contributing

We welcome community contributors to the project. Before you start, please read our code of conduct and check out contributing guidelines first.

Versioning

We document versions and changes in our changelog.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

References

Documentation

Papers

  • Nicholas J Radcliffe and Patrick D Surry. Real-world uplift modelling with significance based uplift trees. White Paper TR-2011-1, Stochastic Solutions, 2011.
  • Yan Zhao, 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, SIAM, 2017.
  • Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 2019.
  • Xinkun Nie and Stefan Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. Atlantic Causal Inference Conference, 2018.

Related projects

  • uplift: uplift models in R
  • grf: generalized random forests that include heterogeneous treatment effect estimation in R
  • rlearner: A R package that implements R-Learner
  • DoWhy: Causal inference in Python based on Judea Pearl's do-calculus
  • EconML: A Python package that implements heterogeneous treatment effect estimators from econometrics and machine learning methods
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