Causal Analysis in Theory and Practice

Dear friends in causality research,

In the past 5 months, since the publication of The Book of Why http://bayes.cs.ucla.edu/WHY/ I have been involved in conversations with many inquisitive readers on Twitter @yudapearl and have not been able to update our blog as frequently as I should. I am glad to return to this forum and update it with the major developments since July, 2018.

1.
Initial reviews of the Book of Why are posted on its trailer page http://bayes.cs.ucla.edu/WHY/ They vary from technical discussions to philosophical speculations, from relationships to machine learning to debates about the supremacy of randomized contolled trials.

2.
A search-able file of all my 750 tweets is available here: https://ucla.in/2Kz0FoY. It can be used for (1) extracting talking points, adages and arguments in the defense of causal inference, and (2) understanding the thinking of neighboring cultures, e.g., statistics, epidemiology, economics, deep learning and reinforcement learning, primarily on issues of transparency, testability, manipulability, do-expressions and counterfactuals.

3.
The 6th printing of the Book Of Why is now available, with corrections to all errors and typos discovered up to Oct. 29, 2018. To check that you have the latest printing, make sure the last line on the copywright page ends with … 8 7 6

4.
Please examine the latest papers and reports from our brewry:

R-484 Pearl, “Causal and Counterfactual Inference,” Forthcoming section in The Handbook of Rationality, MIT press. https://ucla.in/2Iz9myt

R-484 Pearl, “A note on oxygen, matches and fires, On Non-manipulable Causes,” September 2018. https://ucla.in/2Qb1h6v

R-483 Pearl, “Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes,” https://ucla.in/2EpxcNU Journal of Causal Inference, 6(2), online, September 2018.

R-481 Pearl, “The Seven Tools of Causal Inference with Reflections on Machine Learning,” July 2018 https://ucla.in/2umzd65 Forthcoming, Communications of ACM.

R-479 Cinelli and Pearl, “On the utility of causal diagrams in modeling attrition: a practical example,” April 2018. https://ucla.in/2L8KAWw Forthcoming, Journal of Epidemiology.

R-478 Pearl and Bareinboim, “A note on `Generalizability of Study Results’,” April 2018. Forthcoming, Journal of Epidemiology. https://ucla.in/2NIsI6B

Earlier papers can be found here: http://bayes.cs.ucla.edu/csl_papers.html

5.
I wish in particular to call attention to the introduction of R-478, https://ucla.in/2NIsI6B. It provides a “three bullets” recipe for comparing
the structural and potential outcome frameworks:

* To determine if there exist sets of covariates $W$ that satisfy “conditional exchangeability”
** To estimate causal parameters at the target population in cases where such sets $W$ do not exist, and
*** To decide if one’s modeling assumptions are compatible with the available data.

I have listed the “three bullets” above in the hope that they serve to facilitate and concretize future conversations with our neighbors from the potential outcome framework.

6. We are informed of a most relevant workshop: AAAI-WHY 2019, March 26-27, Stanford, CA. The 2019 AAAI Spring Symposium will host a new workshop: Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI. See https://why19.causalai.net. Submissions due December 17, 2018

Greetings and Happy Holidays
Judea