causality

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.

An index of algorithms for learning causality with data

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.

Eliot: the logging system that tells you *why* it happened

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

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

The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML

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

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

Causal Discovery for Python. Translation and extension of the Tetrad Java code.

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

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

A toolbox for integrated information theory.

CausalLift: Python package for causality-based Uplift Modeling in real-world business

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

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

Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

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。

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

[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Causal Inference & Deep Learning, MIT IAP 2018

The official implementation of "Disentangling User Interest and Conformity for Recommendation with Causal Embedding" (WWW '21)

Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893

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

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

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model