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:



  1. Dear Professor Pearl
    I have a question related to page 103 of your book “Causal Inference in Statistics, A Primer”.

    The third paragraph of page 103, referring Figure 4.3 about (X,Z,Y) (education, skill and salary).

    The text said, X and Y (or Yx) is not d-seperated given Z. But, this doesn’t looks right to me. Z indeed doesn’t satisfied the back door condition with respect X,Y), but clearly X and Y are d-seperated given Z.

    The text also said Z is collider between X and U1. This may be a typo for U2?

    I read this third paragraph many times, had hard time to understand it.



    Comment by Songshan Li — May 13, 2016 @ 7:38 pm

  2. Songshan,

    You are correct. U1 shoudl be replaced with U2. The typo will be corrected on the Errata soon. Thank you for pointing this out and please let us know if you find any other mistakes.


    Comment by bryantc — May 18, 2016 @ 2:37 am

RSS feed for comments on this post. TrackBack URI

Leave a comment

Powered by WordPress