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arxausality

A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.

awesome-causality-algorithms

An index of algorithms for learning causality with data

awesome-causality-data

A data index for learning causality.

castle

CASTLE (Causal Structure Learning) regularization

catenets

Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

causalgraphicalmodels

Causal Graphical Models in Python

causality

causalml

Uplift modeling and causal inference with machine learning algorithms

deep-learning-for-causal-inference

Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

dnabert-2-for-understanding-regulatory-sequences

domainbed

DomainBed is a suite to test domain generalization algorithms

dowhy

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.

drnet

💉📈 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.

econml

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.

git-tutorial

git_and_github_introduction

This repository contains the material we prepared for a short hands-on course for introducing Git and GitHub to the participants.

iaf-vae

Pytorch Implementation of OpenAI's "Improved Variational Inference with Inverse Autoregressive Flow"

iap-appbml

Applied Probabilistic Programming & Bayesian Machine Learning (MIT IAP 2017)

iap-cidl

Causal Inference & Deep Learning, MIT IAP 2018

if-learn

Learning algorithms for machine learning based estimation and inference on structural target functions, such as conditional average treatment effects, using influence functions.

invariantriskminimization

PyTorch code to run synthetic experiments.

kl-divergence-estimators

Testing methods for estimating KL-divergence from samples.

ml-from-scratch

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.

mmd

pytorch-flows

PyTorch implementations of algorithms for density estimation

pytorch-vae

A Collection of Variational Autoencoders (VAE) in PyTorch.

realcause

Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causal structure.

robustdg

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

scigan