#Causality
Showing 34 of 34 repositories tagged #causality, ranked by stars
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.
Next generation of automated data exploratory analysis and visualization platform.
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
A Python package for modular causal inference analysis and model evaluations
:exclamation: uplift modeling in scikit-learn style in python :snake:
The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalAI
Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series
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.
Python package for causal discovery based on LiNGAM.
Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.
A curated list of trustworthy deep learning papers. Continually updating...
Dynamic Causality in Rust
A resource list for causality in statistics, data science and physics
A (concise) curated list of awesome Causal Inference resources.
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
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。
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
Python package for Granger causality test with nonlinear forecasting methods.
Streamline a data analysis process
OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.
An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.
Python tools for regression discontinuity designs
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
Notes for Judea Pearl et al., *Causal Inference in Statistics, a Primer*
Machine learning based causal inference/uplift in Python
A python package for modeling, persisting and visualizing causal graphs embedded in knowledge graphs.
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Information-Theoretic Measures for Revealing Variable Interactions
Code for our ICML '19 oral paper: Neural Network Attributions: A Causal Perspective.
A Causal Relation Schema for Text