Causal Analysis in Theory and Practice

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

We have received the following announcement from Isabelle Guyon, regarding a contest on “cause-effect pairs”:

Cause-effect pairs challenge
March 28 – July 19, 2013

For details, please see the following flyer (here) or the link http://www.causality.inf.ethz.ch/cause-effect.php.

We received the following announcement from Robin Evans (University of Cambridge):

Approaches to Causal Structure Learning Workshop – UAI 2013,
July 15, 2013, Bellevue, WA, USA

Additional details can be found here: http://www.statslab.cam.ac.uk/~rje42/uai13/main.htm

A recent discussion on Andrew Gelman’s blog has touched on some interesting points concerning the teaching of causality in econometric and statistics classes (link here). I responded to some of the discussants and, below, I share my replies with readers of this blog.

1. Andrew Gelman asked why the review in http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf is critical of econometrics, “I thought that causality was central to econometrics; see, for example, Angrist and Pischke’s book .”

Judea Pearl replies:
Causality is indeed central to econometrics. Our survey of econometric textbooks http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf is critical of econometric education today, not of econometric methodology proper. Econometric models, from the time of Haavelmo (1943), have been and remained causal (see http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf ) despite two attempted hijacking, first by regressionists, and second by “quasi-experimentalists,” like Angrist and Paschke (AP). The six textbooks we reviewed reflect a painful recovery from the regressionist assault which more or less disappeared from serious econometric research, but is still obfuscating authors of econometric textbooks.

As to the debate between the structuralists and experimentalists, I address it in Section 4 of this article: (see http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf)

Your review of Angrist and Paschke book “Mostly Harmless Econometrics” leaves out what in my opinion is the major drawback of their methodology: sole reliance of instrumental variables and failure to express and justify the assumptions that underlie the choice of instruments. Since the choice of instruments rests on the same type of assumptions (ie.,exclusion and exogeneity) that Angrist and Paschke are determined to avoid (for being “unreliable,) readers are left with no discussion of what assumptions do go into the choice of instruments, how they are encoded in a model, what scientific knowledge can be used to defend them, and whether the assumptions have any testable implications.

In your review, you point out that Angrist and Pischke completely avoid the task of model-building; I agree. And I attribute this avoidance, not to lack of good intentions but to lacking mathematical tools necessary for model-building. Angrist and Pischke have deprived themselves of using such tools by making an exclusive commitment to the potential outcome language, while shunning the language of nonparametric structural models. This is something only he/she can appreciate who attempted to solve a problem, from start to end, in both languages, side by side. No philosophy, ideology, or hours of blog discussion can replace the insight one can gain by such an exercise.

2. A discussant named Jack writes:
An economist (econometrician) friend of mine often corresponds with Prof. Pearl, and what I understand is that Pearl believes the econometrics approach to causality is deeply, fundamentally wrong. (And econometricians tend to think Pearl’s approach is fundamentally wrong.) It sounds to me like Pearl was being purposefully snarky.

Judea Pearl replies:
Jack, I think you misunderstood what your friend told you. If you read my papers and books you will come to realize immediately that I believe the econometrics approach to causality is deeply an fundamentally right (I repeat: RIGHT, not WRONG). Though, admittedly, there have been two attempts to distort this approach by influx of researchers from adjacent fields (see my reply to Andrew on this page, or read http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf

Next, I think you are wrong in concluding that “econometricians tend to think Pearl’s approach is fundamentally wrong”. First, I do not offer anyone “an approach,” I offer mathematical tools to do what researchers say they wish to do, only with less effort and greater clarity; researchers may choose to use or ignore these tools. By analogy, the invention of the microscope was not “an approach” but a new tool.

Second, I do not know a single econometrician who tried my microscope and thought it is “fundamentally wrong”, the dismissals I often hear come invariably from those who refuse to look at the microscope for religious reasons.

Finally, since you went through the trouble of interpreting hearsay and labeling me “purposefully snarky,” I think you owe readers of this blog ONE concrete example where I criticize an economist for reasons that you judge to be unjustified. You be the judge.

3. An Anonymous discussant writes:
Yes, the problem with the econometrics approach is that it lumps together identification, estimation, and probability, so papers look like a Xmas tree. It all starts with chapter 1 in econometrics textbooks and all those assumptions about the disturbance, linearity, etc. Yet most discussions in causality oriented papers revolve around identification and for that you can mostly leave out functional forms, estimation, and probability.

Why carry around reams of parametric notation when it ain’t needed? One wonders how Galileo, Newton, or Franklin ever discovered anything without X’X^(-1)X’Y?

Judea Pearl replies:
To all discussants:
I hear many voices agreeing that statistics education needs a shot of relevancy, and that causality is one area where statistics education has stifled intuition and creativity. I therefore encourage you to submit nominations for the causality in statistics prize, as described in http://www.amstat.org/education/causalityprize/ and http://magazine.amstat.org/blog/2012/11/01/pearl/

Please note that the criteria for the prize do not require fancy formal methods; they are problem-solving oriented. The aim is to build on the natural intuition that students bring with them, and leverage it with elementary mathematical tools so that they can solve simple problems with comfort and confidence (not like their professors). The only skills they need to acquire are: (1) Articulate the question, (2) Specify the assumptions needed to answer it and (3) Determine if the assumptions have testable implications. The reasons we cannot totally dispose of mathematical tools are: (1) scientists have local intuitions about different parts of a problem and only mathematics can put them all together coherently, (2) eventually, these intuitions will need to be combined with data to come up with assessments of strengths and magnitudes (e.g., of effects). We do not know how to combine data with intuition in any other way, except through mathematics.

Recall, Pythagoras theorem served to amplify, not stifle the intuitions of ancient geometers.

We received the following query from Megan Murphy (ASA):
Dr. Pearl,
I received the following question regarding the Causality in Statistics Education prize on twitter. I’m not sure how to answer this, perhaps you can help?

Would entries using Neyman-Rubin model even be considered? RT @AmstatNews: Causality in Statistics Education #prize magazine.amstat.org/blog/2012/11/0…

Judea Answers:
“Of course! The criteria for evaluation specifically state: ‘in some mathematical language (e.g., counterfactuals, equations, or graphs)’ giving no preference to any of the three notational systems. The criteria stress capabilities to perform specific inference tasks, regardless of the tools used in performing the tasks.

For completeness, I re-list below the evaluation criteria:

• The extent to which the material submitted equips students with skills needed for effective causal reasoning. These include:

—1a. Ability to correctly classify problems, assumptions, and claims into two distinct categories: causal vs. associational

—1b. Ability to take a given causal problem and articulate in some mathematical language (e.g., counterfactuals, equations, or graphs) both the target quantity to be estimated and the assumptions one is prepared to make (and defend) to facilitate a solution

—1c. Ability to determine, in simple cases, whether control for covariates is needed for estimating the target quantity, what covariates need be controlled, what the resulting estimand is, and how it can be estimated using the observed data

—1d. Ability to take a simple scenario (or model), determine whether it has statistically testable implications, and apply data to test the assumed scenario

• The extent to which the submitted material assists statistics instructors in gaining an understanding of the basics of causal inference (as outlined in 1a-d) and prepares them to teach these basics in undergraduate and lower-division graduate classes in statistics.

Those versed in the Neyman-Rubin model are most welcome to submit nominations.

Note, however, that nominations will be evaluated on ALL four skills, 1a – 1d.
Judea

The American Statistical Association has announced a new Prize,
“Causality in Statistics Education”, aimed to encourage the teaching of
basic causal inference in introductory statistics courses.

The motivation for the prize is discussed in an interview I gave to Ron Wasserstein:
http://magazine.amstat.org/blog/2012/11/01/pearl/

Nomination procedures and selection criteria can be found here:
http://www.amstat.org/education/causalityprize/

I hope readers of this blog will participate, either by innovating new
ways of teaching causation or by identifying candidates deserving of the prize.
Judea

Dear friends in causality,

I am happy to announce that Manabu Kuroki has received a publication award
for the Japanese edition of my book, “Causality” from The Behaviormetric Society
of Japan. “This award has been given to the authors who had excellent publications
or the translation regarding humanities and social sciences in Japan.”

The provided links of the award are below (sorry it is in Japanese):

The Behaviormetric Society of Japan:
http://www.bsj.gr.jp/about/prize.html

Kyouritsu Shuppan Co, Ltd:
http://www.kyoritsu-pub.co.jp/bookdetail/9784320018778

Our proud congratulations to Manabu for this immense honor.
Judea

Dear friends in causality research,

This communication highlights a few meetings in the summer of 2012, that should be of interest to causality researchers. Naturally, these are biased in favor of those that were brought to my attention. If you know of more such meetings, feel free to post.

1.
July 22-26, 2012, Toronto, Canada
Annual Conference of the Association for Advancement of
Artificial Intelligence, AAAI-12,
http://www.aaai.org/Conferences/AAAI/2012/

I will present a lecture on “The Mechanization of Causal Inference, a Mini Turing-Test and Beyond”`
http://www.aaai.org/Conferences/AAAI/2012/aaai12turing.php
and Elias Bareinboim will present a completeness result for the transportability problem.
http://ftp.cs.ucla.edu/pub/stat_ser/r390.pdf

2.
Joint Statistical Meeting
JSM 2012, San Diego, CA, July 28-Aug 3, 2012
There are 73 papers and meetings on causal inference listed in the program, here they are: http://www.amstat.org/meetings/jsm/2012/onlineprogram/KeywordSearchResults.cfm

These include a day-long course on Targeted Learning: Causal Inference for Observational and Experimental Data
CE_08C Sun, 7/29/2012, 8:30 AM – 5:00 PM HQ-Indigo E
by Maya Petersen, Sherri Rose, Mark van der Laan, http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=304318

And J. Pearl tutorial “Causal Inference in Statistics: A gentle introduction” Sunday, July 17 4-6pm http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=304318

—————An Interesting Observation ——
In 2002, JSM-2002 had only 13 papers on causal inference, By any gauge, 73 is a positive sign of progress in the field.
—————end of interesting observation ——

3.
UAI2012, Catalina, CA, August 15-17, 2012
(Uncertainty in Artificial Intelligence)

Workshop on Causal Structure Learning, Aug. 18
http://www.stat.washington.edu/tsr/uai-causal-structure-learning-workshop/

Alison Gopnik will speak on “Babies, Brain and Bayes (Banquet Speech), Thursday, Aug. 16.,
and I will speak on “Do-Calculus Revisited” Aug. 17, 1:30 pm
http://www.auai.org/uai2012/invited.shtml

4.
Workshop on Networks Processes and Causality
Sept 3-6, 2012, Menorca, Spain
http://people.tuebingen.mpg.de/networks-workshop/

5.
MLSP 2012 Special Session on Causal Discovery
IEEE Workshop on Machine Learning for Signal Processing (MLSP 2012)
September 23-26 2012, Santander, Spain
http://mlsp2012.conwiz.dk/index.php?id=3D62

6.
Symposium on Causal Inference, University of Michigan,
December 12, 2012. Contact: Professor Yu Xie.

Miscellaneous
7.
Larry Wasserman has a new blog, and dedicated a page to befriending “causality”.
http://normaldeviate.wordpress.com/2012/06/18/48/

8.
A new book “Causality: Statistical Perspectives and Applications”,
C. Berzuini, P. Dawid and L. Bernardinelli (Eds.)
has just been published by Wiley (Chihester) July 2012: see
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470665564.html .

Perpetual online access to the book is available on:
http://onlinelibrary.wiley.com/doi/10.1002/9781119945710.fmatter/summary

9.
Another new book appeared this month
R.H. Hoyle (Ed.) Handbook of Structural Equation Modeling, New York: Guilford Press.
It contains my chapter on “The Causal Foundations of SEM”
ttp://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf

10.
Finally, the UCLA fruit basket offers a few fresh items, see http://bayes.cs.ucla.edu/csl_papers.html
One item of interest to economists-educators is:
Chen and Pearl “A Critical Examination of Econometrics Textbooks,” where we survey six influential econometric textbooks in terms of their mathematical treatment of causal concepts. The conclusions are revealing, if you click on http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf (And if you have ideas on reforming econometric education, please share.)

Wishing you an insightful and productive summer
Best,
Judea Pearl

Dear colleagues in causality research,

This is a Winter Greeting from the UCLA Causality blog, welcoming you back to a discussion on causality-related issues.

This message contains
1. Topics under discussion
2. New results
3. Information on journals, lectures, conferences, and software.

1. Topics under Discussion

1.1 Principal Stratification – A goal or a tool?’
http://ftp.cs.ucla.edu/pub/stat_ser/r382.pdf
http://escholarship.org/uc/item/4xj9d380#page-3

Was posted for discussion in the International Journal of Biostatistics (IJB) in March 2011, and has elicited response from eight discussants on whether studies based on Principal Stratification estimate quantities that researchers care about.

I am about to wrap up the discussion with a summary-rejoinder, so, if you have comments or insights that where not brought up, feel free to communicate them to the IJB’s Editor, “Nicholas P. Jewell” or/and, if you wish, cross-post them on this blog.

1.2 Comments and Controversies:
We are being warned again that graphical models can produce “incorrect” causal inferences. The warning comes again from Lindquist and Sobel (LS), entitled “Cloak and DAG”: http://www.sciencedirect.com/science/article/pii/S1053811911013085

A response to L&S is posted on our causality blog, proving them wrong, and questioning the wisdom of asking researchers to translate assumptions from a language where they stand out vividly and meaningfully into an Arrow-Phobic language where they can no longer be recognized, let alone justified. We have all the reason to suspect that L&S will come back.

1.3 The Match-Maker Paradox
An apparent paradox concerning the representation of matching designs in DAGs was posted by Pablo Lardelli and resolved by noting that matching involves unit-to-unit interaction and results in “persistent-unfaithfulness.”

1.4 An On-going Causal-Inference Discussions on SEMNET (Structural Equation Modeling Discussion Group
In the past four months I have spent time discussing modern approaches to causal inference with SEM researchers who, by and large, are still practicing the traditional methods associated with the acronym “SEM”. The discussions are fully documented and archived on http://bama.ua.edu/archives/semnet.html

Topics include:
a. the causal/statistical distinction.
b. the structural-regressional distinction
c. The residual/disturbance distinction
d. The assumptions conveyed by each structural equation.
e. The counterfactual reading of structural equations
f. What the Mediation Formula tells us about mediation and policy questions.
g. Mediators and Moderators.
h. d-separation, equivalent models and the testable implications of structural models
i. The logic of SEM as an inference engine.

Additionally, a weekly session is being conducted by Les Hayduk, going page by page over the R-370 chapter (http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf) and explaining it to novices in the field. It answers, I hope, all questions that rank and file researchers find perplexing when introduced to causal analysis.

1.5 Draft Chapter on Causality and SEM
Ken Bollen and I finished a draft chapter titled “Eight Myths about Causality and Structural Equation Models.” It covers the history of misconceptions about SEM, including recent assaults by the Arrow-Phobic Society.
see http://ftp.cs.ucla.edu/pub/stat_ser/r393.pdf

1.6 A Survey Paper on Adjustment
Greenland, S., and Pearl J., “Adjustments and their Consequences — Collapsibility Analysis using Graphical Models” http://ftp.cs.ucla.edu/pub/stat_ser/r369.pdf

The paper teaches researchers how to glance at a graph and determine when/if an adjustment for one variable modifies the relationship between two other variables. It is a simple exercise for graphical modellers but extremely difficult one for economists and other researchers who ask such questions routinely and have no graphs for guidance.

1.7 A New Introduction to Causal Calculus
An excellent introduction to causal diagrams and do-calculus was posted recently by bloggist-author Michael Nielsen, titled “If correlation doesn’t imply causation, then what does?” It can be accessed here: http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/

My response, together with thoughts on the psychology of Simpson’s Paradox is below http://www.michaelnielsen.org/ddi/guest-post-judea-pearl-on-correlation-causation-and-the-psychology-of-simpsons-paradox/

1.8 Haavelmo and the Emergence of Causal Calculus
http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf

Presented at Haavelmo Centennial Symposium, in Oslo, last December, the paper describes the cultural barriers that Haavelmo’s ideas have had to overcome in the past six decades and points to the fact that modern economists are still unaware of the benefits that Haavelmo’s ideas bestow upon them.

2. New Results in Causal Inference

2.1 Interpretable Conditions for Identifying Natural Direct Effects.
http://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf

The paper lists four conditions that are sufficient for the identification of natural direct and indirect effects. The conditions do not invoke “ignorability” jargon thus permitting more informed judgment of the plausibility of the assumptions. It also shows that conditions usually cited in the literature are overly restrictive, and can be relaxed without compromising identification.

2.2 Some Thoughts on Transfer Learning, with Applications to Meta-analysis and Data-sharing Estimation
http://ftp.cs.ucla.edu/pub/stat_ser/r387.pdf

Summary: How to combine data from multiple and diverse environments so as to take full advantages of that which they share in common.

2.3 Understanding Bias Amplification
http://ftp.cs.ucla.edu/pub/stat_ser/r386.pdf

This note sheds a new light on the phenomenon of “bias amplification” by considering the cumulative effect of conditioning on multiple “near instruments,” and shows that bias amplification may build up at a faster rate than bias reduction.

2.4 Local Characterizations of Causal Bayesian Networks
by Bareinboim, Brito and Pearl http://ftp.cs.ucla.edu/pub/stat_ser/r384.pdf

The standard definition of Causal Bayesian Networks (CBN) requires that every interventional distribution be decomposable into a truncated product, dictated by the graph. This paper replaces this “global” definition with three alternative ones, each invoking “local” aspects of conditioning and intervening.

3. Journals, Courses, Lectures and Conferences
3.1 Tutorial: Causal Inference in Statistics

I will be giving a tutorial (IOL) on causal inference at the upcoming JSM 2012 conference in San Diego California, July 29th, 4-5:50 PM. If any of your students or colleagues wishes to attend, the abstract and further details can be found on http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=304318

3.2 The Journal of Causal Inference
As reported in the last blog email, the Journal of Causal Inference (JCI) was launched on September 2011 and the website is open for submissions. http://www.bepress.com/jci

The first issue is planned for Summer of 2012 and, needless to state, you are invited to submit your latest results, and to bring JCI to the attention of students and colleagues who might be seeking a forum for presenting their latest ideas, results and, yes, breakthroughs!

The Journal of Causal Inference will highlight both the uniqueness and interdisciplinary nature of causal research. and will publish both theoretical and applied research including survey and discussion papers.

3.3 Spring Workshop Graphical Causal Models
Friday 3/30/2012
Northwestern University, Chicago

This workshop will introduce graphical causal models, show how to simulate data from, and estimate such models in Tetrad, explain model search, and more….
Lecturers: Richard Scheines and Joseph Ramsey CMU
For details see chicagochapterasa@gmail.com
http://community.amstat.org/Chicago_Chapter/Calendar/20112012/NewItem6/

3.4 Conference: EVIDENCE AND CAUSALITY IN THE SCIENCE ECitS 2012
Centre for Reasoning, University of Kent, 5-7 September 2012
Organizers: Phyllis Illari and Federica Russo
http://www.kent.ac.uk/secl/philosophy/jw/2012/ecits/

3.5 New Software tool for Causal Inference
DAGitty: A Graphical Tool for Analyzing Causal Diagrams by Textor, Johannes; Hardt, Juliane; Kn|ppel, Sven
Epidemiology: September 2011 – Volume 22 – Issue 5 – p 745 doi: 10.1097/EDE.0b013e318225c2be

This paper announces the release of DAGitty, a graphical user interface for drawing and analyzing causal diagrams. DAGitty, offers several improvements over Kyono’s “COMMENTATOR” http://ftp.cs.ucla.edu/pub/stat_ser/r364.pdf among them efficient listing of all minimal sufficient adjustment sets. It is available under an open-source license obtained at www.dagitty.net and http://www.dagitty.net/manual.pdf

Best wishes,

=======Judea

Dear colleague in causality research,

At long last, I am pleased to bring to your attention the birth of a new journal, the Journal of Causal Inference — JCI, dedicated to building a rigorous cross-disciplinary dialogue in causality.

The first issue is planned for Fall 2011 and the website is now open for submissions.
http://www.bepress.com/jci

As most readers of this forum know, existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression, simultaneous equations or logical implications, and glossing over the special ingredients needed for causal analysis. In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. The journal serves as a forum for the growing causal inference community to develop a shared language and to study the commonalities and distinct strengths of their various disciplines’ methods for causal analysis.

You are invited to submit your latest results, and to bring JCI to the attention of students and colleagues who might be seeking a forum for discussing their latest ideas, results and, yes, breakthroughs!

Submissions
Journal of Causal Inference encourages submission of applied and theoretical work from across the range of rigorous causal paradigms.

In addition to significant original research articles, Journal of Causal Inference also welcomes:
1)      Submissions that synthesize and assess cross-disciplinary methodological research
2)      Submissions that discuss the history of the causal inference field and its philosophical underpinnings
3)      Unsolicited short communications on topics that aim to highlight areas of emerging consensus and ongoing controversy, or to bring unorthodox perspectives to open questions
4)      Responses to published articles in causality

To read more about JCI, including its aims and scope and editorial board membership, please visit our website:
http://www.bepress.com/jci
Papers can be submitted electronically at:
http://www.bepress.com/cgi/submit.cgi?context=jci

The first issue is planned for Fall 2011.

Have a productive Fall quarter,
See you on JCI
=======Judea