Causal Analysis in Theory and Practice

February 12, 2016

Winter Greeting from the UCLA Causality Blog

Friends in causality research,
This greeting from the UCLA Causality blog contains:

A. An introduction to our newly published book, Causal Inference in Statistics – A Primer, Wiley 2016 (with M. Glymour and N. Jewell)
B. Comments on two other books: (1) R. Klein’s Structural Equation Modeling and (2) L Pereira and A. Saptawijaya’s on Machine Ethics.
C. News, Journals, awards and other frills.

Our publisher (Wiley) has informed us that the book “Causal Inference in Statistics – A Primer” by J. Pearl, M. Glymour and N. Jewell is already available on Kindle, and will be available in print Feb. 26, 2016.

This book introduces core elements of causal inference into undergraduate and lower-division graduate classes in statistics and data-intensive sciences. The aim is to provide students with the understanding of how data are generated and interpreted at the earliest stage of their statistics education. To that end, the book empowers students with models and tools that answer nontrivial causal questions using vivid examples and simple mathematics. Topics include: causal models, model testing, effects of interventions, mediation and counterfactuals, in both linear and nonparametric systems.

The Table of Contents, Preface and excerpts from the four chapters can be viewed here:
A book website providing answers to home-works and interactive computer programs for simulation and analysis (using dagitty)  is currently under construction.

We are in receipt of the fourth edition of Rex Kline’s book “Principles and Practice of Structural Equation Modeling”,

This book is unique in that it treats structural equation models (SEMs) as carriers of causal assumptions and tools for causal inference. Gone are the inhibitions and trepidation that characterize most SEM texts in their treatments of causation.

To the best of my knowledge, Chapter 8 in Kline’s book is the first SEM text to introduce graphical criteria for parameter identification — a long overdue tool
in a field that depends on identifiability for model “fitting”. Overall, the book elevates SEM education to new heights and promises to usher a renaissance for a field that, five decades ago, has pioneered causal analysis in the behavioral sciences.

Much has been written lately on computer ethics, morality, and free will. The new book “Programming Machine Ethics” by Luis Moniz Pereira and Ari Saptawijaya formalizes these concepts in the language of logic programming. See book announcement As a novice to the literature on ethics and morality, I was happy to find a comprehensive compilation of the many philosophical works on these topics, articulated in a language that even a layman can comprehend. I was also happy to see the critical role that the logic of counterfactuals plays in moral reasoning. The book is a refreshing reminder that there is more to counterfactual reasoning than “average treatment effects”.

C. News, Journals, awards and other frills.
Nominations are Invited for the Causality in Statistics Education Award (Deadline is February 15, 2016).

The ASA Causality in Statistics Education Award is aimed at encouraging the teaching of basic causal inference in introductory statistics courses. Co-sponsored by Microsoft Research and Google, the prize is motivated by the growing importance of introducing core elements of causal inference into undergraduate and lower-division graduate classes in statistics. For more information, please see .

Nominations and questions should be sent to the ASA office at . The nomination deadline is February 15, 2016.

Issue 4.1 of the Journal of Causal Inference is scheduled to appear March 2016, with articles covering all aspects of causal analysis. For mission, policy, and submission information please see:

Finally, enjoy new results and new insights posted on our technical report page:


December 20, 2014

A new book out, Morgan and Winship, 2nd Edition

Filed under: Announcement,Book (J Pearl),General,Opinion — judea @ 2:49 pm

Here is my book recommendation for the month:
Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research) Paperback – November 17, 2014
by Stephen L. Morgan (Author), Christopher Winship (Author)
ISBN-13: 978-1107694163 ISBN-10: 1107694167 Edition: 2nd

My book-cover blurb reads:
“This improved edition of Morgan and Winship’s book elevates traditional social sciences, including economics, education and political science, from a hopeless flirtation with regression to a solid science of causal interpretation, based on two foundational pillars: counterfactuals and causal graphs. A must for anyone seeking an understanding of the modern tools of causal analysis, and a must for anyone expecting science to secure explanations, not merely descriptions.”

But Gary King puts it in a more compelling historical perspective:
“More has been learned about causal inference in the last few decades than the sum total of everything that had been learned about it in all prior recorded history. The first comprehensive survey of the modern causal inference literature was the first edition of Morgan and Winship. Now with the second edition of this successful book comes the most up-to-date treatment.” Gary King, Harvard University

King’s statement is worth repeating here to remind us that we are indeed participating in an unprecedented historical revolution:

“More has been learned about causal inference in the last few decades than the sum total of everything that had been learned about it in all prior recorded history.”

It is the same revolution that Miquel Porta noted to be transforming the discourse in Epidemiology (link).

Social science and Epidemiology have been spear-heading this revolution, but I don’t think other disciplines will sit idle for too long.

In a recent survey (here), I attributed the revolution to “a fruitful symbiosis between graphs and counterfactuals that has unified the potential outcome framework of Neyman, Rubin, and Robins with the econometric tradition of Haavelmo, Marschak, and Heckman. In this symbiosis, counterfactuals emerge as natural byproducts of structural equations and serve to formally articulate research questions of interest. Graphical models, on the other hand, are used to encode scientific assumptions in a qualitative (i.e. nonparametric) and transparent language and to identify the logical ramifications of these assumptions, in particular their testable implications.”

Other researchers may wish to explain the revolution in other ways; still, Morgan and Winship’s book is a perfect example of how the symbiosis can work when taken seriously.

A new review of Causality

Filed under: Book (J Pearl),General,Opinion — eb @ 2:46 pm

A new review of Causality (2nd Edition, 2013 printing) has appeared in Acta Sociologica 2014, Vol. 57(4) 369-375.
Reviewed by Felix Elwert, University of Wisconsin-Madison, USA.

Elwert highlights specific sections of Causality that can empower social scientists with new insights or new tools for applying modern methods of causal inference in their research. Coming from a practical social science perspective, this review is a welcome addition to the list of 33 other reviews of Causality, which tend to be more philosophical. see

I am particularly gratified by Elwert’s final remarks:
“Pearl’s language empowers social scientists to communicate causal models with each other across sub-disciplines…and enables social scientists to communicate more effectively with statistical methodologists.”

September 11, 2009

Recent Activities in Causality

Filed under: Announcement,Book (J Pearl),Discussion — moderator @ 4:00 am

Judea Pearl writes:

Dear colleagues in causality research,

  1. I am pleased to announce that the 2nd Edition of Causality is out now (I saw a real copy), and should hit your bookstore any day. Thanks for waiting patiently, and I apologize for not having books to sign at the JSM meeting in DC.
  2. You may be pleased to know that, after a long and heated discussion on Andrew Gelman’s website, a provisional resolution (truce?) has been declared on the question: Is there such a thing as overadjustment? Click for details…
  3. A new survey paper, gently summarizing everything I know about causation (in 40 pages) is now posted. Comments are welcome.
  4. A new paper answering the question: “When are two measurements equally valuable for effect estimation?” has been posted. Confession: It is really a neat result.

Wishing you a fruitful new school year and may clarity reign in causality land.

=======Judea Pearl

June 28, 2009

Joint Statistical Meetings 2009

Filed under: Announcement,Book (J Pearl),JSM — moderator @ 10:00 am

Judea Pearl will be presenting a tutorial at the JSM meeting (Washington, DC August 5, 2009 from 2-4pm) on "Causal Analysis in Statistics: A Gentle Introduction"

Additional information about the session may be obtained by clicking here.

Book Signing
Just before the tutorial at 12 noon, there will be a book-signing gathering at the Cambridge University Press booth, where J. Pearl will be signing copies of the 2nd Edition of Causality and will engage in gossip and debates about where causality is heading.

October 22, 2008

Forthcoming 2nd Edition of Causality

Filed under: Announcement,Book (J Pearl) — moderator @ 11:00 pm

The new edition will (1) provide technical corrections, updates, and clarifications to all ten chapters in the original book; (2) add summaries of new developments at the end of each chapter; (3) elucidate subtle issues that readers have found perplexing in a new chapter.

Information about the upcoming release, including an updated table of contents, may be found here:

We welcome your comments.

February 22, 2007

Back-door criterion and epidemiology

Filed under: Back-door criterion,Book (J Pearl),Epidemiology — moderator @ 9:03 am

The definition of the back-door condition (Causality, page 79, Definition 3.3.1) seems to be contrived. The exclusion of descendants of X (Condition (i)) seems to be introduced as an after fact, just because we get into trouble if we dont. Why cant we get it from first principles; first define sufficiency of Z in terms of the goal of removing bias and, then, show that, to achieve this goal, you neither want nor need descendants of X in Z.

October 15, 2006

The validity of G-estimation

Filed under: Book (J Pearl),G-estimation — moderator @ 12:00 pm

From a previous correspondence with Eliezer S. Yudkowsky, Research Fellow, Singularity Institute for Artificial Intelligence, Santa Clara, CA

The following paragraph appears on p. 103, shortly after eq. 3.63 in my copy of Causality:

"To place this result in the context of our analysis in this chapter, we note that the class of semi-Markovian models satisfying assumption (3.62) corresponds to complete DAGs in which all arrowheads pointing to Xk originate from observed variables."

It looks to me like this is a sufficient, but not necessary, condition to satisfy 3.62. It appears to me that the necessary condition is that no confounder exist between any Xi and Lj with i < j and that no confounder exist between any Xi and the outcome variable Y. However, a confounding arc between any Xi and Xj, or a confounding arc between Li and Xj with i <= j, should not render the causal effect non-identifiable. For example, even if a confounding arc exists between X2 and X3 (but no other confounding arcs exist in the model), the causal effect on Y of setting X2=x2 and X3=x3 should be the same as the distribution on Y if we observe x2 and x3.

It is also not necessary that the DAG be complete.

May 8, 2006

Identifying conditional plans

Filed under: Book (J Pearl),Plans — moderator @ 12:00 am

Section 4.2 of the book (p. 113) gives an identification condition and estimation formula for the effect of a conditional action, namely, the effect of an action do(X=g(z)) where Z is a measurement taken prior to the action. Is this equation generalizable to the case of several actions, i.e., conditional plan?

The difficulty seen is that this formula was derived on the assumption that X does not change the value of Z. However, in a multi-action plan, some actions in X could change observations Z that are used to guide future actions. We do not have notation for distinguishing post-intevention from pre-intevention observations.

December 1, 2000

The causal interpretation of structural coefficients

Filed under: Book (J Pearl),structural equations — moderator @ 12:00 am

From L. H., University of Alberta and S.M., Georgia Tech 

In response to my comments (e.g., Causality, Section 5.4) that the causal interpretation of structural coefficients is practically unknown among SEM researchers, and my more recent comment that a correct causal interpretation is conspicuously absent from all SEM books and papers, including all 1970-1999 texts in economics, two readers wrote that the "unit-change" interpretation is common and well accepted in the SEM literature.

L.H. from the University of Alberta wrote:
"Page 245 of L. Hayduk, Structural Equation Modeling with LISREL: Essentials and Advances, 1986, has a chapter headed "Interpreting it All", whose first section is titled "The basics of interpretation," whose first paragraph, has a second sentence which says in italics (with notation changed to correspond to the above) that a slope can be interpreted as: the magnitude of the change in y that would be predicted to accompany a unit change in x with the other variables in the equation left untouched at their original values." … "Seems to me that O.D. Duncan, Introduction to Structural Equation Models 1975 pages 1 and 2 are pretty clear on b as causal. "More precisely, it [byx] says that a change of one unit in x … produces a change of b units in y" (page 2). I suspect that H. M. Blalock's book "Causal models in the social Sciences", and D. Heise's book "Causal analysis." probably speak of b as causal."

S.M., from Georgia Tech concurs:
"I concur with L.H. that Heise, author of Causal Analysis (1975) regarded the b of causal equations to be how much a unit change in a cause produced an effect in an effect variable. This is a well-accepted idea."

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