Coz: Causal Profiling

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

Uplift modeling and causal inference with machine learning algorithms

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.

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

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.

Python Causal Impact port of Google's Algorithm.

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

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

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

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

Statistical Rethinking (2nd ed.) with NumPyro

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

❗ uplift modeling in scikit-learn style in python 🐍

Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction

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

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

Variable importance through targeted causal inference, with Alan Hubbard

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

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

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

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

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

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

Policy learning via doubly robust empirical welfare maximization over trees

🎯🎓 Generalized Targeted Learning Framework