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.

An index of algorithms for learning causality with data

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

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

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

A toolbox for integrated information theory.

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

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

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

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.

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

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

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.

Causal Inference & Deep Learning, MIT IAP 2018

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

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

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。

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

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

Automated Storytelling via Causal, Commonsense Plot Ordering

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

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

Implementation of the EMNLP 2020 paper "Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition".

Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)

Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling