HumesGuillotine
causal-learn
| HumesGuillotine | causal-learn | |
|---|---|---|
| 3 | 1 | |
| 1 | 992 | |
| - | 3.8% | |
| 4.8 | 7.9 | |
| 30 days ago | 27 days ago | |
| Python | ||
| - | MIT License |
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HumesGuillotine
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Learning Universal Predictors
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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Elon Musk proposes that a new version of quantum mechanics/cosmology, will be derived, possibly by using his version of artificial intelligence "xAI".
See Hume's Guillotine at github for what Musk should be pursuing.
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Market price of power, as produced by the Suncell will not be very low, for a long time
This is one of the reasons I've been advocating a philanthropic prize for macrosocial modeling: Ockham's Guillotine: Beheading the social pseudosciences.
causal-learn
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
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.
Rath - Next generation of automated data exploratory analysis and visualization platform.
dodiscover - [Experimental] Global causal discovery algorithms
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
tfcausalimpact - Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
sections - Easy Python tree data structures
Eliot - Eliot: the logging system that tells you *why* it happened
looper - A resource list for causality in statistics, data science and physics
Structure_threader - A wrapper program to parallelize and automate runs of "Structure", "fastStructure" and "MavericK".