causallift
cdci-causality
| causallift | cdci-causality | |
|---|---|---|
| 1 | 2 | |
| 333 | 3 | |
| - | - | |
| 1.3 | 10.0 | |
| about 1 year ago | about 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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causallift
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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.
cdci-causality
What are some alternatives?
causalml - Uplift modeling and causal inference with machine learning algorithms
dodiscover - [Experimental] Global causal discovery algorithms
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.
alibi - Algorithms for explaining machine learning models
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
pysyncon - A python module for the synthetic control method