Judea Pearl, UCLA professor, winner of the Turing Prize in Computer Science, is a hero to all Bayesian Network fans like me. Pearl has several books on B nets,

as you can see at his Amazon page.. This blog post is to alert my readers to his most recent book, written in collaboration with Dana Mackenzie, released about a week ago, mid May 2018, entitled

“The Book of Why: The New Science of Cause and Effect”.

To commemorate the release of the new book, I also wrote, besides this blog post, a small comment about the new book at the Edward Forum, and Dustin Tran, main author of Edward, responded with a comment that cites a very nice paper, less than 6 months old, by Dustin and Prof. Blei, Dustin’s thesis advisor at Columbia Univ, about the use of Judea Pearl’s causality ‘do-calculus’ within Edward.

I’ve been interested in the do-calculus for a long time, and have written two arxiv papers on the subject:

- Introduction to Judea Pearl’s Do-Calculus, by Robert R. Tucci (Submitted on 26 Apr 2013)
- An Information Theoretic Measure of Judea Pearl’s Identifiability and Causal Influence, by Robert R. Tucci (Submitted on 21 Jul 2013)

This paper is for classical Bayesian Networks, but it can easily be generalized to quantum Bayesian Networks, by replacing probability distributions by density matrices in the information measure proposed there.

There exist more than a dozen packages written in R that implement at least partially the do-calculus. They are available at CRAN (the main R repository, named after cranberries).

This 2017 paper contains a nice table of various R packages dealing with do-calculus.

It’s also interesting to note that BayesiaLab, a commercial software package that I love and recommend, already implements some of Pearl’s do-calculus. (full disclosure: the B net company that I work at, artiste-qb.net, has no business connections with BayesiaLab.)

By the way, artiste-qb.net provides a nice cloud service that allows you to run all these open-source do-calculus R packages on your browser, without any installation hassles. How? you ask, and if not, I’m going to tell you anyway.

***Beep, Beep, Commercial Alert***

artiste-qb.net is a multilingual (R, Python, Java, C++, English, German, Spanish, Chinese, Italian, French, you name it, we speak it) quantum open source software company.

We offer an image on AWS (the Amazon cloud service) called BayesForge.com.

BayesForge.com comes fully loaded with the Python distribution Anaconda, all of R, etc.

Bayesforge comes with most major Artificial Intelligence/Bayesian Networks, open-source packages installed, both classical ones (eg. TensorFlow, Edward, PyMC, bnlearn, etc) and quantum ones (eg., IBM Qiskit, DWave stuff, Rigetti and Google stuff, our own Quantum Fog, Quantum Edward, Qubiter, etc).

BayesForge allows you to run jupyter notebooks in Python, R, Octave (an open source matlab clone) and Bash. You can also combine Python and R within one notebook using Rmagic.

We have succeeded in dockerzing the BayesForge image and will be offering it very soon on other cloud services besides AWS, including a non-AWS cloud service in China, where AWS is so slow it is non-usable. One of our co-founders, Dr. Tao Yin, lives in ShenZhen, China, and is in charge of our China branch.

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