genome_integration VS causal-learn

Compare genome_integration vs causal-learn 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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genome_integration causal-learn
1 1
11 988
- 3.4%
0.0 7.9
almost 2 years ago 24 days ago
Python Python
MIT License 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.

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.

causal-learn

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

What are some alternatives?

When comparing genome_integration and causal-learn you can also consider the following projects:

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.

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

dodiscover - [Experimental] Global causal discovery algorithms

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

tfcausalimpact - Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

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

sections - Easy Python tree data structures

causalml - Uplift modeling and causal inference with machine learning algorithms

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

HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.