Popular GitHub Repositories for Causality

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.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

[Embodied-AI-Survey-2024] Paper list and projects for Embodied AI

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Curated research at the intersection of causal inference and natural language processing.

A Python package for modular causal inference analysis and model evaluations

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.

A (concise) curated list of awesome Causal Inference resources.

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Python package for the creation, manipulation, and learning of Causal DAGs

Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Causal Inference & Deep Learning, MIT IAP 2018

An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.

A General Causal Inference Framework by Encoding Generative Modeling

Amortized Inference for Causal Structure Learning, NeurIPS 2022

Machine learning based causal inference/uplift in Python

(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models