Causality

Open-source projects categorized as Causality
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Language: + Python + Jupyter Notebook + TypeScript + JavaScript + R
Topics: causal-inference Machine Learning causal-discovery Python causal-models
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Top 20 Causality Open-Source Projects

  • 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.

    • Project mention: Causality for Machine Learning | news.ycombinator.com | 2023-09-26

    I'm a fan of the Do Why library out of Microsoft. Even as a novice in the field of causal modeling it can get you up and running by estimating the causal graph based on your data. https://github.com/py-why/dowhy

  • Rath

    Next generation of automated data exploratory analysis and visualization platform.

    • Project mention: FLaNK Stack for 15 May 2023 | dev.to | 2023-05-15

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  • 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

  • awesome-causality-algorithms

    An index of algorithms for learning causality with data

    • Project mention: Why the world needs computational social science | news.ycombinator.com | 2023-10-02

    https://github.com/rguo12/awesome-causality-algorithms

    "The limits of graphical causal discovery" (2021)

  • pgmpy

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

  • Eliot

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

  • causal-learn

    Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • causalML

    The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML (by altdeep)

  • pyphi

    A toolbox for integrated information theory.

    • Project mention: Is Conway's Game of Life Conscious According to Integrated Information Theory? | /r/askscience | 2023-06-05

    it's not very hard to build a model (and the corresponding transition probability matrix) for a GoL network. and the version 4.0 formalism code is online for anyone to use (https://github.com/wmayner/pyphi). so you could try to answer the question for yourself (though it gets computationally prohibitive for networks bigger than 10 units or so, so...)

  • causallift

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

  • looper

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

    • Project mention: A resource list for causality in statistics, data science and physics | news.ycombinator.com | 2023-09-02

  • causalai

    Salesforce CausalAI Library: A Fast and Scalable framework for Causal Analysis of Time Series and Tabular Data

  • CausalWorld

    CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

  • causaldag

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

  • pycopent

    Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

  • dodiscover

    [Experimental] Global causal discovery algorithms

  • causalgraph

    A python package for modeling, persisting and visualizing causal graphs embedded in knowledge graphs.

  • copent

    R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test

  • cdci-causality

    Python implementation of CDCI, a method to identify causal direction between two variables

  • HumesGuillotine

    Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.

    • Project mention: Learning Universal Predictors | news.ycombinator.com | 2024-01-31

    As the guy who suggested to Marcus a lossless compression prize to replace the Turing Test, I've got to confess that all this pedantic sophistry "critiquing" algorithmic information is there for a good reason. In the immortal words of Mel Brooks: "We've got to protect our phoney baloney jobs gentlemen!"

    https://youtu.be/bpJNmkB36nE

    There is actually more at stake here than machine learning. This gets to the root of "bias" in the scientific method. Imagine what horrors, what risks, what chaos would be ours if a truly objective information criterion for causal model selection were to exist! Why, virtually every "sociologist" would be hauled to Hume's Guillotine in a Reign of Terror!

    https://github.com/jabowery/HumesGuillotine

    But to be clear, Marcus and I have a disagreement about pragmatics of such an approach to dispute processing in the natural sciences. He believes, for example, that the dispute over climate change should be handled by the standard processes in place with academia. My approach differs, based on my hard won experience with reform reforming institutional incentives:

    https://jimbowery.blogspot.com/2018/04/necessity-and-incenti...

    When it comes to multi-trillion dollar scientific questions, the conflicts of interest become so intense that you really need to apply a gold standard for objectivity and that is the single number: How big is your executable archive of the data in evidence.

    While I understand the machine learning world looms as a rival for "unbiased" academic research, it nevertheless remains true that even in this emerging "marketplace of ideas", there is no formal definition of "bias" that disciplines discourse and thereby guides development at the institutional, let alone technical level. Everyone is weighing in with their fuzzy notions of "bias" that betray intense motivations when there has been, for over 50 years, a very clear and present mathematical definition.

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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

Causality related posts

Index

What are some of the best open-source Causality projects? This list will help you:

Project Stars
1 dowhy 6,722
2 Rath 3,965
3 EconML 3,540
4 awesome-causality-algorithms 2,789
5 pgmpy 2,617
6 Eliot 1,083
7 causal-learn 978
8 causalML 696
9 pyphi 353
10 causallift 333
11 looper 235
12 causalai 226
13 CausalWorld 201
14 causaldag 133
15 pycopent 132
16 dodiscover 57
17 causalgraph 40
18 copent 38
19 cdci-causality 3
20 HumesGuillotine 1

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