genome_integration
causalnex
| genome_integration | causalnex | |
|---|---|---|
| 1 | 2 | |
| 11 | 2,147 | |
| - | 1.2% | |
| 0.0 | 5.4 | |
| almost 2 years ago | 22 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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genome_integration
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[D] Clustering high dimensional
Causal Genomic Analysis
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).
What are some alternatives?
CausalPy - A Python package for causal inference in quasi-experimental settings
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.
enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
causalml - Uplift modeling and causal inference with machine learning algorithms
awesome-causality-algorithms - An index of algorithms for learning causality with data
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
causaldag - Python package for the creation, manipulation, and learning of Causal DAGs
looper - A resource list for causality in statistics, data science and physics