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.

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

An index of algorithms for learning causality with data

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

A toolbox for integrated information theory.

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

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

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

CausalLift: Causality-based Uplift Modeling in real-world business

❗ uplift modeling in scikit-learn style in python 🐍

Causal Inference & Deep Learning, MIT IAP 2018

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

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

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

Python tools for regression discontinuity designs

Causal Deconvolution of Networks by Algorithmic Generative Models

A causal-consistent debugger for Erlang

Causal Inference with Invariant Prediction

Regulatory networks with Direct Information in python

The Machine Learning Toybox for testing the behavior of autonomous agents.

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。

Accompanying code for the paper "Learning Causal Models Online"

A Brief Overview of Causal Inference (xaringan presentation)

Improved sEMG-based gesture recognition

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

Events and Situations Ontology

Notes for Judea Pearl et al., *Causal Inference in Statistics, a Primer*

Causal Simulations for Uplift Modeling

Estimates the inference of a Fuzzy Cognitive Map (FCM). Provides a selection of 6 different inference rules and 4 threshold functions in order to obtain the inference of the FCM.

Machine learning based causal inference/uplift in Python