Pinned Repositories
causallib
A Python package for modular causal inference analysis and model evaluations
gene-benchmark
Benchmark gene representations from different model families
Epilepsy-causal
Code snippets showing how to use causallib to perform causal inference analysis on epilepsy outcomes from EHR and claims data
IBM-Causal-Inference-Benchmarking-Framework
Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code
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.
linux-files
My startup linux files and accesories
phenoClust
R implementation of phenoClust
Random_Genes
The code that generated the analysis presented in the manuscript "Association Between Expression of Random Gene Sets and Survival is Evident in Multiple Cancer Types and May be Explained by Sub-Classification"
scikit-learn
scikit-learn: machine learning in Python
yishaishimoni's Repositories
yishaishimoni/Random_Genes
The code that generated the analysis presented in the manuscript "Association Between Expression of Random Gene Sets and Survival is Evident in Multiple Cancer Types and May be Explained by Sub-Classification"
yishaishimoni/phenoClust
R implementation of phenoClust
yishaishimoni/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.
yishaishimoni/linux-files
My startup linux files and accesories
yishaishimoni/scikit-learn
scikit-learn: machine learning in Python