causal-inference

Coz: Causal Profiling

Uplift modeling and causal inference with machine learning algorithms

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian 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.

An index of algorithms for learning causality with data

A Python library that helps data scientists to infer causation rather than observing correlation.

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

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

Resources to learn more about Machine Learning and Artificial Intelligence

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

Causal Discovery for Python. Translation and extension of the Tetrad Java code.

↙️ ↘️ An R package for working with causal directed acyclic graphs (DAGs)

Statistical Rethinking (2nd ed.) with NumPyro

Open-source Python library for statistical analysis of randomised control trials (A/B tests)

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 Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

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

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

Must-read papers and resources related to causal inference and machine (deep) learning

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction

A selection of state-of-the-art research materials on decision making and motion planning.

Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

DoubleML - Double Machine Learning in Python

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

Causal inference, graphical models and structure learning with the PC algorithm.

➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.