EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x. (by py-why)
causalml
Uplift modeling and causal inference with machine learning algorithms (by uber)
| EconML | causalml | |
|---|---|---|
| 8 | 10 | |
| 3,550 | 4,763 | |
| 1.3% | 1.1% | |
| 8.5 | 8.5 | |
| 8 days ago | 8 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
EconML
Posts with mentions or reviews of EconML.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-13.
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[D] What approach to decide which class is most optimal for recovery?
A good package with many of the tools used for this type of problem as well as pretty good documentation about how it all works is https://github.com/microsoft/EconML
- Getting treatment effects from a random forest
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
Github: https://github.com/microsoft/EconML
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UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
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[q] before/after test
EconML and CausalLift are pretty good python packages that help you build uplift models. scikit-uplift is a decent sklearn style wrapper package that can be helpful as well. One of the drawbacks of these packages is they only allow for the modeling of a single treatment. mr-uplift is a newer package that allows you to model the multiple treatment effects simultaneously. I haven't used it personally, but it does look fairly interesting.
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[N] Spotify Confidence - open source for analyzing a/b test data
Can't see how this adds to decades of causal inference packages development in stats oriented frameworks like R/Stata/EViews etc and the ongoing effort of porting this to Python. If you want something fancy there's already EconML.
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What are some applications of Data Science in Digital Marketing?
Uplift Modeling - This is a very powerful technique aimed at discovering the customers who are most likely to respond to your marketing efforts. Some good python libraries for this are EconML and mr-uplift
causalml
Posts with mentions or reviews of causalml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-13.
- uber/causalml: Uplift modeling and causal inference with machine learning algorithms
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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Completed 3 months in Microsoft as Data Scientist.
Do you (or Microsoft DS teams in general) tackle any causal problems? Apparently, uber tries to solve these issues, they created https://github.com/uber/causalml
- [R] apd-crs: Cure Rate Survival Analysis in Python
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[S] Python packages to replace R
There's some causality focused packages. Nothing like scikit-learn or statsmodels yet because it's all based on very recent research, but this one: https://github.com/uber/causalml
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Good way to segment a/b test results for insight or narrative?
I agree that uplift trees and CATE methods are promising here. If you’re in Python, check out Uber’s open-source causalml.
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UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
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R, I love you.
you like causal inference? it must be nice to be able to use libraires like dowhy, causal ml, and ananke right? 🤔🤔🤔
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Causal data science
video's author recommends this course in a comment: https://www.coursera.org/learn/crash-course-in-causality and he also co-created this library: https://github.com/uber/causalml
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Model Re-Training with Intervention Effects
There aren't many general solutions because it will really depend on your domain. There are some packages that specifically work for "modeling under interventions" (like this https://github.com/uber/causalml although I have never tried it). In general, if you have enough data between intervention and not-intervention, you could train two different models and then apply whichever one makes sense (e.g. if you wanted to find the highest-churn-risk users among those who have already had the intervention, use the model trained on the prior intervention cases).
What are some alternatives?
When comparing EconML and causalml you can also consider the following projects:
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
tensor-house - A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
BTYD - BTYD 2.4.3
Robyn - Robyn is a Super Fast Async Python Web Framework with a Rust runtime.
CausalPy - A Python package for causal inference in quasi-experimental settings