causalml
upliftml
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causalml | upliftml | |
---|---|---|
10 | 7 | |
4,698 | 306 | |
3.3% | 2.3% | |
8.4 | 0.0 | |
5 days ago | about 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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causalml
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
- [R] apd-crs: Cure Rate Survival Analysis in Python
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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? 🤔🤔🤔
upliftml
What are some alternatives?
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.
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
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.
BTYD - BTYD 2.4.3
BTYDplus - R package for Customer Behavior Analysis
CausalPy - A Python package for causal inference in quasi-experimental settings
genome_integration - MR-link and genome integration. genome_integration is a repository for the analysis of genomic data. Specifically, the repository implements the causal inference method MR-link, as well as other Mendelian randomization methods.
db-benchmark - reproducible benchmark of database-like ops