Popular GitHub Repositories for Causal Responsibility

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.

Uplift modeling and causal inference with machine learning algorithms

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.

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

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

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

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

DoubleML - Double Machine Learning in Python

面向中文大模型价值观的评估与对齐研究

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

Library implementing the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

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

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

Time series prediction using dilated causal convolutional neural nets (temporal CNN)

A selection of state-of-the-art research materials on decision making and motion planning.

Causal discovery algorithms and tools for implementing new ones

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