├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2020, Jeff Hale 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | 1. Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | 2. Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | 3. Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Causal inference-related packages 3 | 4 | ### Please submit a pull request with any additions! 😀 5 | --- 6 | 7 | - [DoWhy](https://github.com/Microsoft/dowhy) - making causal inference easy (Microsoft) ![GitHub Repo stars](https://img.shields.io/github/stars/Microsoft/dowhy?style=social) 8 | - [CausalML](https://github.com/uber/causalml) - suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research (Uber) ![GitHub Repo stars](https://img.shields.io/github/stars/uber/causalml?style=social) 9 | - [cfrnet](https://github.com/clinicalml/cfrnet) - counterfactual regression (doesn't appear to be actively developed) ![GitHub Repo stars](https://img.shields.io/github/stars/clinicalml/cfrnet?style=social) 10 | - [Causality](https://github.com/akelleh/causality) - tools for causal analysis using observational (rather than experimental) datasets (doesn't appear to be actively developed) ![GitHub Repo stars](https://img.shields.io/github/stars/akelleh/causality?style=social) 11 | - [Causal Discovery Toolbox](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) - causal inference in graphs and pairwise settings ![GitHub Repo stars](https://img.shields.io/github/stars/FenTechSolutions/CausalDiscoveryToolbox?style=social) 12 | - [CausalDAG](https://github.com/uhlerlab/causaldag) - creation, manipulation, and learning of Causal DAGs ![GitHub Repo stars](https://img.shields.io/github/stars/uhlerlab/causaldag?style=social) 13 | - [CausalNex](https://github.com/quantumblacklabs/causalnex) - uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning ![GitHub Repo stars](https://img.shields.io/github/stars/quantumblacklabs/causalnex?style=social) 14 | - [Causal Inference](https://github.com/laurencium/Causalinference) - implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis ![GitHub Repo stars](https://img.shields.io/github/stars/laurencium/Causalinference?style=social) 15 | - [EconML](https://github.com/microsoft/EconML) - estimating heterogeneous treatment effects from observational data via machine learning (Microsoft) ![GitHub Repo stars](https://img.shields.io/github/stars/microsoft/EconML?style=social) 16 | - [scikit-uplift](https://github.com/maks-sh/scikit-uplift) - classic approaches for uplift modeling built on top of scikit-learn ![GitHub Repo stars](https://img.shields.io/github/stars/maks-sh/scikit-uplift?style=social) 17 | - [Networkx](https://github.com/networkx/networkx) - for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks ![GitHub Repo stars](https://img.shields.io/github/stars/networkx/networkx?style=social) 18 | 19 | --- 20 | ### Hat tip sources: 21 | - [Awesome Causalit](https://github.com/napsternxg/awesome-causality#tools) 22 | - [Awesome Open Source](https://awesomeopensource.com/projects/causal-inference) 23 | - [Introduction to Causal Inference](https://www.bradyneal.com/causal-inference-course) by Brady Neal 24 | - An Hoang for the suggestion to use [Shields.io](https://shields.io/) 25 | --------------------------------------------------------------------------------