CausalLift: Python package for Uplift Modeling in real-world business; applicable for both A/B testing and observational data
If you are simply building a Machine Learning model and executing promotion campaigns to the customers who are predicted to buy a product, for example, it is not efficient.
Some customers will buy a product anyway even without promotion campaigns (called "Sure things").
It is even possible that the campaign triggers some customers to churn (called "Do Not Disturbs" or "Sleeping Dogs").
The solution is Uplift Modeling.
Uplift Modeling is a Machine Learning technique to find which customers (individuals) should be targeted ("treated") and which customers should not be targeted.
Uplift Modeling is also known as persuasion modeling, incremental modeling, treatment effects modeling, true lift modeling, net modeling.
Applications of Uplift Modeling for business include:
- Increase revenue by finding which customers should be targeted for advertising/marketing campaigns and which customers should not.
- Retain revenue by finding which customers should be contacted to prevent churn and which customers should not.
The most famous use case of Uplift Modeling would be the 44th US president Barack Obama's 2nd presidential campaign in 2012. Obama's team used Uplift Modeling to find which voters could be persuaded to vote for him. Here are some articles.
- What is ‘Persuasion Modeling’, and how did it help Obama to win the elections?
- How Obama’s Team Used Big Data to Rally Voters
- How uplift modeling helped Obama's campaign -- and can aid marketers
Uplift Modeling estimates uplift scores (a.k.a. CATE: Conditional Average Treatment Effect or ITE: Individual Treatment Effect). Uplift score is how much the estimated conversion rate will increase by the campaign.
Suppose you are in charge of a marketing campaign to sell a product, and the estimated conversion rate (probability to buy a product) of a customer is 50 % if targeted and the estimated conversion rate is 40 % if not targeted, then the uplift score of the customer is (50–40) = +10 % points. Likewise, suppose the estimated conversion rate if targeted is 20 % and the estimated conversion rate if not targeted is 80%, the uplift score is (20–80) = -60 % points (negative value).
The range of uplift scores is between -100 and +100 % points (-1 and +1). It is recommended to target customers with high uplift scores and avoid customers with negative uplift scores to optimize the marketing campaign.
- CausalLift works with both A/B testing results and observational datasets.
- CausalLift can output intuitive metrics for evaluation.
In a word, to use for real-world business.
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Existing packages for Uplift Modeling assumes the dataset is from A/B Testing (a.k.a. Randomized Controlled Trial). In real-world business, however, observational datasets in which treatment (campaign) targets were not chosen randomly are more common especially in the early stage of evidence-based decision making. CausalLift supports observational datasets using a basic methodology in Causal Inference called "Inverse Probability Weighting" based on the assumption that propensity to be treated can be inferred from the available features.
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There are 2 challenges of Uplift Modeling; explainability of the model and evaluation. CausalLift utilizes a basic methodology of Uplift Modeling called Two Models approach (training 2 models independently for treated and untreated samples to compute the CATE (Conditional Average Treatment Effects) or uplift scores) to address these challenges.
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[Explainability of the model] Since it is relatively simple, it is less challenging to explain how it works to stakeholders in the business.
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[Explainability of evaluation] To evaluate Uplift Modeling, metrics such as Qini and AUUC (Area Under the Uplift Curve) are used in research, but these metrics are difficult to explain to the stakeholders. For business, a metric that can estimate how much more profit can be earned is more practical. Since CausalLift adopted the Two-Model approach, the 2 models can be reused to simulate the outcome of following the recommendation by the Uplift Model and can estimate how much conversion rate (the proportion of people who took the desired action such as buying a product) will increase using the uplift model.
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Table data including the following columns:
- Features
- a.k.a independent variables, explanatory variables, covariates
- e.g. customer gender, age range, etc.
- Note: Categorical variables need to be one-hot coded so propensity can be estimated using logistic regression. pandas.get_dummies can be used.
- Outcome: binary (0 or 1)
- a.k.a dependent variable, target variable, label
- e.g. whether the customer bought a product, clicked a link, etc.
- Treatment: binary (0 or 1)
- a variable you can control and want to optimize for each individual (customer)
- a.k.a intervention
- e.g. whether an advertising campaign was executed, whether a discount was offered, etc.
- Note: if you cannot find a treatment column, you may need to ask stakeholders to get the data, which might take hours to years.
- [Optional] Propensity: continuous between 0 and 1
- propensity (or probability) to be treated for observational datasets (not needed for A/B Testing results)
- If not provided, CausalLift can estimate from the features using logistic regression.
Example table data
Option 1: install from the PyPI
pip3 install causalliftOption 2: install from the GitHub repository
pip3 install git+https://github.com/Minyus/causallift.gitOption 3: clone the GitHub repository, cd into the downloaded repository, and run:
python setup.py installOption 1: install from the GitHub repository
pip3 install git+git://github.com/Minyus/[email protected]Option 2: clone v1.0 branch of the GitHub repository,
cd into the downloaded repository, and run:
python setup.py install- numpy
- pandas
- scikit-learn
- easydict
- kedro>=0.15.0
- matplotlib
- xgboost
- scikit-optimize
Please see the demo code in Google Colab (free cloud CPU/GPU environment) :
To run the code, navigate to "Runtime" >> "Run all".
To download the notebook file, navigate to "File" >> "Download .ipynb".
Here are the basic steps to use.
""" Step 0. Import CausalLift
"""
from causallift import CausalLift
""" Step 1. Feed datasets and optionally compute estimated propensity scores
using logistic regression if set enable_ipw = True.
"""
cl = CausalLift(train_df, test_df, enable_ipw=True)
""" Step 2. Train 2 classification models (XGBoost) for treated and untreated
samples independently and compute estimated CATE (Conditional Average Treatment
Effect), ITE (Individual Treatment Effect), or uplift score.
"""
train_df, test_df = cl.estimate_cate_by_2_models()
""" Step 3. Estimate how much conversion rate will increase by selecting treatment
(campaign) targets as recommended by the uplift modeling.
"""
estimated_effect_df = cl.estimate_recommendation_impact()
CausalLift flow diagram
CausalLift version 1.0.0 adopted Kedro to add the following new features.
- [Parallel execution] Train the 2 models in parallel
- [File management] Save and load intermediate files such as the trained models
- [Documentation] Generate the API document by Sphinx and visualize the process flow
Other enhancements include:
- [Logging] Show and/or log processing status such as timestamp and the running task
- [Model options] Specify models other than XGBoost and Logistic Regression for uplift modeling and propensity modeling, respectively.
Please see [CausalLift API reference].
- Python 3.5
- Python 3.6
- Python 3.7
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Uplift Modeling based on Transformed Outcome method for A/B Testing data and visualization of metrics such as Qini.
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"EconML" (ALICE: Automated Learning and Intelligence for Causation and Economics) [documentation]
Several advanced methods to estimate CATE from observational data.
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Visualization of steps in Causal Inference for observational data.
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Propensity Score Matching for observational data.
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Platform for adaptive experiments, powered by BoTorch, a library built on PyTorch
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Uplift Modeling.
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Uplift Modeling and utility tools for quantization of continuous variables, visualization of metrics such as Qini, and automatic feature selection.
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Propensity Score Matching for observational data.
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"CausalImpact" [documentation]
Causal inference using Bayesian structural time-series models
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Gutierrez, Pierre. and G´erardy, Jean-Yves. Causal inference and uplift modelling: A review of the literature. In International Conference on Predictive Applications and APIs, pages 1–13, 2017.
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Athey, Susan and Imbens, Guido W. Machine learning methods for estimating heterogeneous causal effects. Stat, 2015.
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Yi, Robert. and Frost, Will. (n.d.). Pylift: A Fast Python Package for Uplift Modeling. Retrieved April 3, 2019, from https://tech.wayfair.com/2018/10/pylift-a-fast-python-package-for-uplift-modeling/
- [Medium article] Uplift Models for better marketing campaigns (Part 1)
- [Medium article] Simple Machine Learning Techniques To Improve Your Marketing Strategy: Demystifying Uplift Models
- [Wikipedia] Uplift_modelling
- Improve documentation
- Clarify the model summary output including visualization
- Add examples of applying uplift modeling to more publicly available datasets (such as Lending Club Loan Data as pymatch did.
- Support for multiple treatments
Any feedback is welcome!
Please create an issue for questions, suggestions, and feature requests.
Please open pull requests to improve documentation, usability, and features against v1.0 branch.
(v0.0 is no longer active.)
Separate pull requests for each improvement are appreciated rather than a big pull request. It is encouraged to use:
- Google-style docstrings
- PEP 484 comment-style type annotation although Python 2 is not supported.
- An intelligent IDE e.g. PyCharm
If you could write a review about CausalLift in any natural languages (English, Chinese, Japanese, etc.) or implement similar features in any programming languages (R, SAS, etc.), please let me know. I will add the link here.
[English] Causal Inference, Counterfactual, Propensity Score, Econometrics
[中文] 因果推论, 反事实, 倾向评分, 计量经济学
[日本語] 因果推論, 反事実, 傾向スコア, 計量経済学
Yusuke Minami