Causal Analysis in Theory and Practice

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:
http://magazine.amstat.org/blog/2012/11/01/pearl/
http://magazine.amstat.org/blog/2013/08/01/causality-in-stat-edu/.

Nominations and questions should be sent to the ASA office at .
Visit http://www.amstat.org/education/causalityprize/ 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:
http://tiny.cc/jci_2_1 ,
while the first two issues are here:
http://www.degruyter.com/view/j/jci
(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:
http://bayes.cs.ucla.edu/BOOK-09/causality2-errata-updated7_3_13.pdf

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.
http://www.edge.org/response-detail/25435

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”
http://www.darpa.mil/NewsEvents/Releases/2014/02/20.aspx

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),
http://andrewgelman.com/2014/02/09/keli-liu-xiao-li-meng-simpsons-paradox/
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
http://ftp.cs.ucla.edu/pub/stat_ser/r414.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/R264.pdf

As an aside, Johannes Textor informs me that the Simpson’s machine described in r414.pdf is now available on
http://dagitty.net/learn/simpson/
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?”
http://andrewgelman.com/2014/01/16/22571/

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 http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf

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:
http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php

* Video of a tutorial on “Causes and Counterfactuals” presented at NIPS-2013 (by Pearl and Bareinboim) is available here:
http://research.microsoft.com/apps/video/default.aspx?id=206977

* Video of a lecture presented at Columbia University Institute for Data Sciences is available here:
http://idse.columbia.edu/seminarvideo_judeapearl

* Video of a public lecture presented at NYU-Poly is available here:
http://www.livestream.com/poly/video?clipId=pla_b641858e-d11d-4a48-91a3-ff9bd8ead4b6

9. New scientific questions and some of their solutions.
There are new postings on our home page
http://bayes.cs.ucla.edu/csl_papers.html
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.
http://ftp.cs.ucla.edu/pub/stat_ser/r415.pdf

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.
http://ftp.cs.ucla.edu/pub/stat_ser/r421.pdf

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.
http://ftp.cs.ucla.edu/pub/stat_ser/r422.pdf

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

Judea

Dear friends in causality,

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

1. Journal of Causal Inference – Vol. 1, Issue 2
The second issue of the Journal of Causal Inference is on its way, and an on-line posting date has been set for December 23 2013. The first issue, can be viewed here:
http://www.degruyter.com/view/j/jci.2013.1.issue-1/issue-files/jci.2013.1.issue-1.xml
or here:
http://www.degruyter.com/view/j/jci

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

2. Causality book – new printing
The 3rd printing of Causality (2009, 2nd ed.) is finally out (as of Sept. 1), corrected and improved.

If you have an older printing and do not wish to buy another copy, all changes are marked in red here:
http://bayes.cs.ucla.edu/BOOK-09/causality2-errata-updated7_3_13.pdf

3. Special Issue on Counterfactuals
The latest issue of Cognitive Science is dedicated to counterfactual reasoning. Edited by Stephen Sloman, the Table of Content and on-line version are available here:
http://onlinelibrary.wiley.com/doi/10.1111/cogs.2013.37.issue-6/issuetoc

4. A Strange article in Science Magazine.
Two articles in Science Magazine and Nature were brought to our attention:
http://www.sciencemag.org/content/338/6106/496.abstract
http://www.nature.com/nphys/journal/v8/n12/full/nphys2497.html
The author claims that causal effects can be inferred from correlation, using an extended version of Granger Causality.
To me this sounds like squaring the circle; perhaps one of our readers can illuminate us.

5. Calls for papers on Causality
5.1 Isabell Guyon sent us a call for papers for Special Topic of JMLR on Causality and Experimental Design. See:
http://clopinet.com/isabelle/Projects/NIPS2013/Causality_Special_Topic_2014.html

5.2 The ACM TIST journal is planning a Special Issue on Causal Discovery and Inference, and has issued a call for papers.
See: http://tist.acm.org/CFPs/TIST-SI-CDI.html

6. Dennis Lindley, dead at 90
On a sad note, Dennis Lindley, a pioneer in Bayesian inference died last week, at age 90.
Lindley brought the “SEEING vs. DOING” distinction to the attention of the statistics community:
http://bayes.cs.ucla.edu/BOOK-2K/lindley-rev.pdf
He also adapted the causal interpretation of Simpson’s paradox ahead of his peers.
We will miss his intellect, curiosity and integrity.
A true gentleman.

7. New postings on this blog.
Since our last greetings, the following items were posted on this blog (you can view them below).
Aug. 9 , 2013, Larry Wasserman on JSM 2013
Oct. 26, 2013, Comments on Kenny’s Summary of Causal Mediation
Nov. 10, 2013, On Heckman and Pinto
Nov. 19, 2013, The Key to Understanding Mediation
Dec. 14 2013, “But where does the graph come from?”

8. New slides and videos available
* Slides of the tutorial on “Causes and Counterfactuals” preseted at NIPS-2013 (by Pearl and Bareinboim) are available here:
http://ftp.cs.ucla.edu/pub/stat_ser/nips-dec2013-pearl-bareinboim-tutorial.pdf
http://www.cs.ucla.edu/~eb/nips-dec2013-pearl-bareinboim-tutorial-full.pdf

* Video of an introductory lecture presented to economists (at Stanford) is available here:
http://gsb-mediasite.stanford.edu/mediasite/Play/4ee5b390c7ef456c9ba0b11d1a519d4b1d

9. New scientific questions and some answers
There are new postings on my home page
http://bayes.cs.ucla.edu/csl_papers.html
which might earn your attention. Among them:

420 – J. Pearl, “Reflections on Heckman and Pinto’s ‘Causal Analysis after Haavelmo”,
where I defend Haavelmo’s original theory of intervention against a Fisherian surrogate proposed in Heckman and Pinto (2013).
http://ftp.cs.ucla.edu/pub/stat_ser/r420.pdf

419 – Bareinboin, Lee, Honavar, Pearl “Transportability from Multiple Enironments with Limited Experiments”
where we ask (and answer) whether it is possible to combine experimental findings from many heterogeneous studies to get what we need.
http://ftp.cs.ucla.edu/pub/stat_ser/r419.pdf

417 – Pearl and Mohan “Recoverability and Testability of Missing Data.”
where we explain missing-data problems to the uninitiated using graphical models, and illustrate the concepts of recoverability and testability.
http://ftp.cs.ucla.edu/pub/stat_ser/r417.pdf

416 – J. Pearl “The Mathematics of Causal Inference”
A summary of a Lecture given at JSM-2013, which compiles the main mathematical results in causal inference.
http://ftp.cs.ucla.edu/pub/stat_ser/r416.pdf

414 – J. Pearl, “Understanding Simpson’s Paradox”
where I introduce a guessing game that exhibits perpetual reversals and argue that the paradox can safely be titled: “resolved”
http://ftp.cs.ucla.edu/pub/stat_ser/r414.pdf

410 – Mohan, Pearl and Tian “Graphical Models for Inference with Missing Data” (Newly Revised)
where we take a fresh look at missing data problems from a causal inference perspective and propose a new taxonomy for misssing data mechanisms.
http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf

363 – Pearl and Paz, “Confounding Equivalence in Causal Analysis” (Revised Oct 2013)
We ask: When would an adjustment for T introduce the same bias as an adjustment for Z, and we answer it by extending the results of the 2010 version of this paper.
http://ftp.cs.ucla.edu/pub/stat_ser/r343w.pdf

Wishing you a happy and productive new year,
Judea Pearl

Judea Pearl Writes:

Researchers using causal diagrams have surely noticed that, despite a tremendous progress in causal modeling in the past three decades, editors and reviewers persist in raising questions about the usefulness of causal diagrams, noting that their structure is based largely on untested or untestable assumptions and, hence, that they could not serve as a basis for policy evaluation or personal decision-making.

Questions such as:
“What if I do not have the graph?”
“What if I am not sure about the absence of this or that confounder?”
“What if I do not have the scientific knowledge required to construct the graph,”
and more, come up again and again, especially from editors and reviewers who have not had first-hand experience in causal inference research.

As a service to readers of this blog, I would like to share the way I usually answer such questions.

To continue to the full post, click here.

Judea Pearl Writes:

For a long time I could not figure out why SEM researchers find it hard to embrace the “causal inference approach” to mediation, which is based on counterfactuals. My recent conversations with David Kenny and Bengt Muthen have opened my eyes, and I am now pretty sure that I have found both the obstacle and the key to making causal mediation an organic part of SEM research.

Here is the key:

Why are we tempted to “control for” the mediator M when we wish to estimate the direct effect of X on Y? The reason is that, if we succeed in preventing M from changing then whatever changes we measure in Y are attributable solely to variations in X and we are justified then in proclaiming the effect observed as “direct effect of X on Y”. Unfortunately , the language of probability theory does not possess the notation to express the idea of “preventing M from changing” or “physically holding M constant”. The only operation probability allows us to use is “conditioning” which is what we do when we “control for M” in the conventional way (i.e., let M vary, but ignore all samples except those that match a specified value of M). This habit is just plain wrong, and is the mother of many confusions in the practice of SEM.

To find out why, you are invited to visit: http://ftp.cs.ucla.edu/pub/stat_ser/r421.pdf, paragraph starting with “In the remaining of this note, …”, on page 2.

Best,
Judea

A recent article by Heckman and Pinto (HP) (link: http://www.nber.org/papers/w19453.pdf) discusses the do-calculus as a formal operationalization of Haavelmo’s approach to policy intervention. HP replace the do-operator with an equivalent operator, called “fix,” which simulates a Fisherian experiment with randomized “do”. They advocate the use of “fix,” discover limitations in “do,” and inform readers that those limitations disappear in “the Haavelmo approach.”

I examine the logic of HP’s paper, its factual basis, and its impact on econometric research and education (link: http://ftp.cs.ucla.edu/pub/stat_ser/r420.pdf).

David Kenny’s website <http://davidakenny.net/cm/mediate.htm> has recently been revised to include a section on the Causal Inference Approach to Mediation. As many readers know, Kenny has pioneered mediation analysis in the social sciences through his seminal papers with Judd (1981) and Baron(1986) and has been an active leader in this field. His original approach, often referred to as the “Baron and Kenny (BK) approach,” is grounded in conservative Structural Equation Modeling (SEM) analysis, in which causal relationships are asserted with extreme caution and the boundaries between statistical and causal notions vary appreciably among researchers.

It is very significant therefore that Kenny has decided to introduce causal mediation analysis to the community of SEM researchers which, until very recently, felt alienated from recent advances in causal mediation analysis, primarily due to the counterfactual vocabulary in which it was developed and introduced. With Kenny’s kind permission, I am posting his description below, because it is one of the few attempts to explain causal inference in the language of traditional SEM mediation analysis and, thus, it may serve to bridge the barriers between the two communities.

Next you can find Kenny’s new posting, annotated with my comments. In these comments, I have attempted to further clarify the bridges between the two cultures; the “traditional” and the “causal.” I will refer to the former as “BK” (for Baron and Kenny) and to the latter as “causal” (for lack of a better word) although, conceptually, both BK and SEM are fundamentally causal.

Click here for the full post.

UCLA Department of Statistics Seminar Series

Thu, 10/24/2013, 12:30 PM—1:30 PM
4660 Geology Bldg.

Judea Pearl and Joakim Ekstrom
Statistics
UCLA

A Conversation on Statistical Methodology, with Judea Pearl
http://statistics.ucla.edu/seminars/2013-10-24/12:30pm/4660-geology

Join us for a conversation on statistical methodology, and in particular the theory of causal inference. In this ‘Socratic dialogue’-styled conversation, recent A.M. Turing award winner Judea Pearl will discuss his views on statistical methodology with conversational partner Joakim Ekstrom. The conversation will start at R.A. Fisher’s randomization procedure for isolation of contributors to systematic variation, and then continue discussing the methodology of Judea Pearl for isolation and identification of causal factors in data obtained from sources other than perfectly randomized experiments.

In the conversation, there will be plenty of opportunity for attendees to ask questions, explore alternatives and raise objections, especially regarding ways of introducing causal inference in statistics education.

Judea Pearl is a Professor at UCLA Computer Science and Statistics, and has contributed greatly to the theory of causal inference. Joakim Ekstrom is a post-doctoral research scholar at UCLA Statistics, seminar co-organizer, and an expert on the theory and history of statistics.

We received the following announcement from Richard Scheines (Carnegie Mellon University):

CMU Workshop: Case Studies of Causal Discovery with Model Search, 

October 25-27, 2013, Pittsburgh, PA, USA.

Additional details can be found here: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php

Larry Wasserman posted the following comments on his “normal-deviate” blog:
http://normaldeviate.wordpress.com/2013/08/09/the-jsm-minimaxity-and-the-language-police/

I am back from the JSM (http://www.amstat.org/meetings/jsm/2013/). For those who don’t know, the JSM is the largest statistical meeting in the world. This year there were nearly 6,000 people.

*******skipping *******
On Tuesday, I went to Judea Pearl’s medallion lecture, with discussions by Jamie Robins and Eric Tchetgen Tchetgen. Judea gave an unusual talk, mixing philosophy, metaphors (eagles and snakes can’t build microscopes) and math. Judea likes to argue that graphical models/structural equation models are the best way to view causation. Jamie and Eric argued that graphs can hide certain assumptions and that counterfactuals need to be used in addition to graphs.
***********more *********

J. Pearl:

I posted the following reply:

Larry,

Your note about my Medallion Lecture (at JSM 2013) may create the impression that I am against the use of counterfactuals.

This is not the case.

1. I repeatedly say that counterfactuals are the building blocks of rational behavior and scientific thoughts.
see: http://ftp.cs.ucla.edu/pub/stat_ser/R269.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf

2. I showed that ALL counterfactuals can be encoded parsimoniously in one structural equation model, and can be read easily from any such model.
see: http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf

3. I showed how the graphical-counterfactual symbiosis can work to unleash the merits of both. And I emphasized that mediation analysis would still be in its infancy if it were not for the algebra of counterfactuals (as it emerges from structural semantics.)

4. I am aware of voiced concerns about graphs hiding assumptions, but I prefer to express these concerns in terms of “hiding opportunities”, rather than “hiding assumptions” because the latter is unnecessarily alarming.

A good analogy would be Dawid’s notation X||Y for independence among variables, which states that every event of the form X = x_i is independent of every event of the form Y=y_j. There may therefore be hundreds of assumptions conveyed by the innocent and common statement X||Y.

Is this a case of hiding assumptions?
I do not believe so.

Now imagine that we are not willing to defend the assumption “X = x_k is independent of Y=y_m” for some specific k and m. The notation forces us to write “variable X is not independent of variable Y” thus hiding all the (i,j) pairs for which the independence is defensible. This is a loss of opportunity, not a hiding of assumptions, because refraining from assuming independence is a more conservative strategy; it prevents unwarranted conclusions from being drawn.

Thanks for commenting on my lecture.

Dear friends in causality,

This greeting from the UCLA Causality blog contains:
1. News items concerning causality research
2. Postings and publications

1.1. Journal of Causal Inference – Vol. 1, Issue 1
The first issue of the Journal of Causal Inference is now out, and can be viewed at
http://www.degruyter.com/view/j/jci.2013.1.issue-1/issue-files/jci.2013.1.issue-1.xml
or
http://www.degruyter.com/view/j/jci

Printed copies of this issue will be available at the JSM meeting in Montreal. The 2nd issue is planned for December, 2013. Submissions are welcome on all aspects of causal analysis, especially those deemed impossible.

1.2. Causality in Statistical Education Prize
Congratulations are due to professor Felix Elwert (U. Wisconsin) who won the first Causality in Statistics Education Award from the American Statistical Association this past week. See http://www.amstat.org/education/causalityprize/.
Professor Elwert has earned this honor for his two-day course on “Causal Inference with Directed Acyclic Graphs.”
For slides and course material see http://www.ssc.wisc.edu/~felwert/causality.

Professor Elwert will receive the $5,000 prize and a plaque at the JSM meeting in Montreal, August 4, 2013.

A visionary gift from Microsoft Research will double the prize next year; 5K for a graduate level and 5K for undergraduate.

1.3. Causality book – new printing
The 2nd edition of Causality (2009) is currently going through its 3rd printing, corrected and improved. Printed copies will be available at JSM. If you have an older printing and do not wish to buy another copy, all changes are marked in red here: http://bayes.cs.ucla.edu/BOOK-09/causality2-errata-updated7_3_13.pdf. I also learned that the book is now available on Kindle. Enjoy.

1. 4. Workshops
A workshop on “Approaches to Causal Structure Learning” will be conducted on Monday July 15th in Bellevue, WA, USA. as part of the UAI 2013 conference. For details, see http://www.statslab.cam.ac.uk/~rje42/uai13/main.htm

1.5. Tutorials
The NIPS-2013 conference will host a 2-hour tutorial on causal inference, given by J. Pearl and E. Earenboim December, 5, 2013, Lake Tahoe, Nevada.
For details visit http://nips.cc/Conferences/2013/

2. New postings and publications
2.1. Larry Wasserman has a nice discussion on Simpson’s paradox posted on his blog.
http://normaldeviate.wordpress.com/2013/06/20/simpsons-paradox-explained/

2.2. A recent publication worth noting is S. Morgan (Ed.) “Handbook of Causal Analysis for Social Research 2013,”

2.3. There are a few new postings on my home page:
http://bayes.cs.ucla.edu/csl_papers.html
among them:
410- Mohan, Pearl and Tian “Missing data as a causal inference problem.”
http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf
408 – Bareinboim and Pearl “Causal Transportability with Limited Experiments”
http://ftp.cs.ucla.edu/pub/stat_ser/r408.pdf
406 – Pearl, “Detecting latent heterogeneity”
http://ftp.cs.ucla.edu/pub/stat_ser/r406.pdf
405 – Pearl “A solution to a class of selection-bias problems”
http://ftp.cs.ucla.edu/pub/stat_ser/r405.pdf
393 – Bollen and Pearl “Eight Myths about Causality”
http://ftp.cs.ucla.edu/pub/stat_ser/r393.pdf

Best,
Judea Pearl