alibi
causallift
| alibi | causallift | |
|---|---|---|
| 4 | 1 | |
| 2,289 | 333 | |
| 0.6% | - | |
| 7.7 | 1.3 | |
| 8 days ago | 12 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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alibi
- Alibi: Open-source Python lib for ML model inspection and interpretation
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
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Best alternatives to 'shap' package?
Alibi explain might be an option depending on what you are looking for https://github.com/SeldonIO/alibi
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.
What are some alternatives?
interpret - Fit interpretable models. Explain blackbox machine learning.
causalml - Uplift modeling and causal inference with machine learning algorithms
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
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.
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
conductor - Conductor is a microservices orchestration engine.
dodiscover - [Experimental] Global causal discovery algorithms
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
cdci-causality - Python implementation of CDCI, a method to identify causal direction between two variables
MindsDB - The platform for customizing AI from enterprise data
pysyncon - A python module for the synthetic control method