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A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
An index of algorithms for learning causality with data
A data index for learning causality.
CASTLE (Causal Structure Learning) regularization
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
Causal Graphical Models in Python
Uplift modeling and causal inference with machine learning algorithms
Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.
This repository contains implementations and illustrative code to accompany DeepMind publications
DomainBed is a suite to test domain generalization algorithms
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.
💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.
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.
This repository contains the material we prepared for a short hands-on course for introducing Git and GitHub to the participants.
Pytorch Implementation of OpenAI's "Improved Variational Inference with Inverse Autoregressive Flow"
Applied Probabilistic Programming & Bayesian Machine Learning (MIT IAP 2017)
Causal Inference & Deep Learning, MIT IAP 2018
Learning algorithms for machine learning based estimation and inference on structural target functions, such as conditional average treatment effects, using influence functions.
PyTorch code to run synthetic experiments.
Testing methods for estimating KL-divergence from samples.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
PyTorch implementations of algorithms for density estimation
A Collection of Variational Autoencoders (VAE) in PyTorch.
Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causal structure.
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.