Top 7 causal-model Open-Source Projects

dowhy

8 6,722 8.9 Python

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

pgmpy

2 2,617 8.0 Python

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

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Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
causalnex

2 2,140 5.4 Python

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

2 235 7.3

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

causaldag

1 133 0.0 JavaScript

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

1 17 0.9 TypeScript

JupyterLab renderer of dagitty causal diagrams
HumesGuillotine

3 1 4.8

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.

WorkOS

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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).