Causal Analysis in Theory and Practice

April 1, 2014

Spring Greetings from UCLA Causality Blog

Filed under: General — eb @ 5:00 pm

Dear friends in causality research,

This greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, publications, slides and videos,
C. Debates, controversies and strange articles,
D. New scientific questions and some answers.

1. Nominations are invited for the 2nd ASA “Causality in Statistical Education” Award.
The deadline is April 15, and the background information can be viewed here:

Nominations and questions should be sent to the ASA office at .
Visit for nomination information.

Note: This year, the Award carries a $10,000 prize, which may be split into two $5,000 prizes.

2. Journal of Causal Inference – Vol. 2, Issue 1
The third issue of the Journal of Causal Inference is on its way, and a posting date has been set for April 15th, 2014.
The table of content can be viewed here: ,
while the first two issues are here:
(click on READ CONTENT, under the cover picture)

As always, submissions are welcome on all aspects of causal analysis, especially those deemed heretical.

3. Causality book – 2nd Edition, 3rd printing
Many have been asking how to ensure that the copy they get is the latest, and not some earlier printing of Causality (2009).
The trick is to examine the copyright page and make sure it says: “Reprinted with corrections 2013”

Again, if you have an older printing and do not wish to buy another copy, all changes are marked in red here:

4. Causality is Dead
If we thought that Bertrand Russell’s dismissal of causality as “a relic of a bygone age” was a passing episode — we were
wrong. Danny Hillis has a new essay nominating causality as the one scientific tenet that ought to be discarded.

His bottom line: “We will come to appreciate that causes and effects do not exist in nature, that they are just convenient creations of our own minds.”

I for one would rather explore the cognitive and computational advantages of these “convenient creations” than speculate on their non-existence in nature (see Causality page 419-420). The same goes for “free will”, “explanation”, “responsibility”, “agency”, “credit and blame” and other convenient creations that make up what we call “the understanding.”

5. Causality is Alive
Contrasting Hillis non-existence theory, we were delighted last month to get an existence proof from DARPA (Defence Advanced Research Projects Agency), announcing a new research program entitled Big Mechanism, or, Big Mechanism Seeks the “Whys” Hidden in Big Data”

In a nutshell, this program aims to “leapfrog state-of-the-art big data analytics by developing automated technologies to help explain the causes and effects that drive complicated systems.” At the end of the announcement we read a familiar and visionary prediction: “By emphasizing causal models and explanation, Big Mechanism may be the future of science.”

I dont think many on this list would object to this prediction, though we are perhaps in the best position to appreciate the difficulties.

6. Simpson’s paradox, a new debate
A lively debate on Simpson’s paradox broke out again last month on Andrew Gelman’s blog (95 comments),
triggered by four papers on the subject published in The American Statistician (February, 2014).

The debate raged among three camps.
a) Those who think Simpson’s paradox occurs when “regression coefficients change if you add more predictors,” Therefore, no causality is needed, except that some regressors are “somehow wrong” and others are somehow right.

b) Those who think that “peeling away the paradox is as easy (or hard) as avoiding a comparison of apples and oranges, a concept requiring no mention of causality.”

c) Those (including this writer) who believe that intuitive notions such as “somehow wrong” and “apples and oranges” emanate from the causal structure of the story behind the data and, therefore, are all derivable mechanically from the causal graph. See

As an aside, Johannes Textor informs me that the Simpson’s machine described in r414.pdf is now available on
for users to play with for fun and profit. Enjoy!

7. Who is a Bayesian?
Another lively debate (105 comments) addressed the 250 year old question: “Who is a Bayesian?”

Some think that “Bayes is the analysis of subjective beliefs” and some think that “Bayes is using Bayes rule”, be it with beliefs or with frequencies. My own opinion is summarized as:

“Bayes means:
(1) using knowledge we possess prior to obtaining data,
(2) encoding such knowledge in the language of probabilities
(3) combining those probabilities with data and
(4) accepting the combined results as a basis for decision making and performance evaluation.”
More in

However, my main point was that, rather than arguing about who deserves the honor of being a “Bayesian,” we should discuss what methods better utilize prior knowledge, regardless of whether it is encoded as probabilities or as causal stories.

8. New slides and videos available
* Richard Scheines informed me that slides and videos for the workshop on graphical causal model search at CMU (Oct. 2013) are now available at:

* Video of a tutorial on “Causes and Counterfactuals” presented at NIPS-2013 (by Pearl and Bareinboim) is available here:

* Video of a lecture presented at Columbia University Institute for Data Sciences is available here:

* Video of a public lecture presented at NYU-Poly is available here:

9. New scientific questions and some of their solutions.
There are new postings on our home page
that might earn your attention. Among them:

R-415 “On the Testability of Models with Missing Data”
in which we address the question of whether any data-generating model can be submitted to statistical test, once data are corrupted by missingness. The answer turns out to be positive, and we present sufficient conditions for testability in all three categories: MCAR, MAR and NMAR.

R-421 “Reply to Commentary by Imai, Keele, Tingley and Yamamoto, concerning Causal Mediation Analysis”.
It clarifies how Structural Causal Models (SCM) unify the graphical and potential outcome frameworks, and why ignorability-based assumptions require graphical interpretations before they can be judged for plausibility. It also explains why traditional mediation analysts are so reluctant to adopt modern methods of causal mediation; I blame habitual addiction to Bayes conditionalization for this resistance.

R-422 “Is Scientific Knowledge useful for Policy Analysis? A Peculiar Theorem says: No”
We ask: Why is it that knowing the effect of smoking on cancer does not help us assess the merits of of banning cigarette advertisement.
We speculate on the ramification of this peculiarity in nonparametric analysis.

10. Wishing you a happy and productive spring,
and may your deeds go for a good cause.


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    Comment by Candy — December 9, 2014 @ 2:32 am

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