looper VS awesome-causality-algorithms

What are some alternatives?

Data-science-best-resources - Carefully curated resource links for data science in one place

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.

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

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

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.

spotlight - Deep recommender models using PyTorch.

causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

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

datascience - Curated list of Python resources for data science.

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