causalnex
causaldag
| causalnex | causaldag | |
|---|---|---|
| 2 | 1 | |
| 2,144 | 133 | |
| 1.0% | 0.0% | |
| 5.4 | 0.0 | |
| 14 days ago | about 1 year ago | |
| Python | JavaScript | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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causalnex
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How many of you still buy and read textbooks after your degree?
I don't claim to defend that this is actually the right way of dealing with those things, but QuantumBlack gave a talk at neurips a couple years back, and really hyped up their package (https://github.com/quantumblacklabs/causalnex) for dealing with this stuff.
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What are some tools/best practices that Causal Inferencing teams use for experimentation?
As for causal libraries I'd recommend CausalNex, it's the only library that I know that does Judea Pearl's do() operator, and I think that's really great if you want to intervene over causal knowledge (that you'll want).
causaldag
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Any methods or tools for virtual gene knock-out in single cell RNA seq data?
I am interested in finding out bioinformatically, a causal relationship between an upstream gene (Notch2) and a transcription factor downstream. Is there any other tool other than scTenifoldpy, to perform a virtual knock-down of genes of interest and see which other genes are affected? Is there also any other tool than causaldag that can help infer causal relationships between gene expressions?
What are some alternatives?
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.
scTenifoldpy - A python package implements scTenifoldnet and scTenifoldknk
causalml - Uplift modeling and causal inference with machine learning algorithms
ims - 📚 Introduction to Modern Statistics - A college-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference. For v1, see https://openintro-ims.netlify.app.
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
scikit-learn - scikit-learn: machine learning in Python
looper - A resource list for causality in statistics, data science and physics
Keras - Deep Learning for humans
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
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.
auton-survival - Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
cobaya - Code for Bayesian Analysis