genome_integration VS awesome-causality-algorithms

What are some alternatives?

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

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

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

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

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

spotlight - Deep recommender models using PyTorch.

causalml - Uplift modeling and causal inference with machine learning algorithms

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

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.

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

causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.