causalnex VS genome_integration

Compare causalnex vs genome_integration and see what are their differences.

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. (by adriaan-vd-graaf)
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causalnex genome_integration
2 1
2,157 11
1.6% -
5.4 0.0
14 days ago almost 2 years ago
Python Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

causalnex

Posts with mentions or reviews of causalnex. We have used some of these posts to build our list of alternatives and similar projects.

genome_integration

Posts with mentions or reviews of genome_integration. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing causalnex and genome_integration you can also consider the following projects:

dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

CausalPy - A Python package for causal inference in quasi-experimental settings

causalml - Uplift modeling and causal inference with machine learning algorithms

enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

awesome-causality-algorithms - An index of algorithms for learning causality with data

scikit-learn - scikit-learn: machine learning in Python

causaldag - Python package for the creation, manipulation, and learning of Causal DAGs

looper - A resource list for causality in statistics, data science and physics