Top 7 causal-discovery Open-Source Projects
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causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
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InfluxDB
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.
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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
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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
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HumesGuillotine
Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: A resource list for causality in statistics, data science and physics | news.ycombinator.com | 2023-09-02
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.
causal-discovery related posts
Index
What are some of the best open-source causal-discovery projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Rath | 3,987 |
| 2 | causal-learn | 988 |
| 3 | looper | 236 |
| 4 | pycopent | 134 |
| 5 | copent | 38 |
| 6 | transferentropy | 37 |
| 7 | HumesGuillotine | 1 |
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