EconML
causalglm
EconML | causalglm | |
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8 | 2 | |
3,557 | 17 | |
1.5% | - | |
8.5 | 0.0 | |
2 days ago | about 2 years ago | |
Jupyter Notebook | R | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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EconML
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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
causalglm
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[Q] Sensitivity of (Causal) Inference to Nonlinear Functional Form
Why not both? https://tlverse.org/causalglm/ (Will replace this with a more informative comment when I have free time later today)
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[Q] Should G-methods, IPTW always be used over traditional regression?
This package: https://github.com/tlverse/causalglm was recently developed to fill the gap between fully black box causal learning methods for heterogeneous treatment effects and fully parametric generalized linear model approaches. It allows for both semiparametric and nonparametric robust causal inference for user defined “working parametric models” for the estimands of interest. It is still black box in that non relevant features of the data distribution are estimated using machine learning but the relevant conditional parameters are modeled fully parametrically (with nonparametric robust inference when misspecified). It is very new so use with caution.
What are some alternatives?
causalml - Uplift modeling and causal inference with machine learning algorithms
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
mlr3learners - Recommended learners for mlr3
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.
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
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.
expotools - Useful methods for Exposome research.
Robyn - Robyn is a Super Fast Async Python Web Framework with a Rust runtime.
looper - A resource list for causality in statistics, data science and physics