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:


UAB’s Nutrition Obesity Research Center — Causal Inference Course

Filed under: Announcement,Uncategorized — bryantc @ 1:03 am
We received the following announcement from Richard F. Sarver (UAB):

UAB’s Nutrition Obesity Research Center invite you to join them at one or both of our five-day short courses at the University of Alabama at Birmingham.

June: The Mathematical Sciences in Obesity Research The mathematical sciences including engineering, statistics, computer science, physics, econometrics, psychometrics, epidemiology, and mathematics qua mathematics are increasingly being applied to advance our understanding of the causes, consequences, and alleviation of obesity. These applications do not merely involve routine well-established approaches easily implemented in widely available commercial software. Rather, they increasingly involve computationally demanding tasks, use and in some cases development of novel analytic methods and software, new derivations, computer simulations, and unprecedented interdigitation of two or more existing techniques. Such advances at the interface of the mathematical sciences and obesity research require bilateral training and exposure for investigators in both disciplines. July: Strengthening Causal Inference in Behavioral Obesity Research Identifying causal relations among variables is fundamental to science. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Understanding which social and behavioral factors cause variations in adiposity and which other factors cause variations is vital to producing, evaluating, and selecting intervention and prevention strategies. In addition, developing a greater understanding of obesity’s causes, requires input from diverse disciplines including statistics, economics, psychology, epidemiology, mathematics, philosophy, and in some cases behavioral or statistical genetics. However, applying techniques from these disciplines does not involve routine well-known ‘cookbook’ approaches but requires an understanding of the underlying principles, so the investigator can tailor approaches to specific and varying situations. For full details of each of the courses, please refer to our websites below: Mon 6/13/2016 – Fri 6/17/2016: The Mathematical Sciences in Obesity, Mon 7/25/2016 – Fri 7/29/2016: Strengthening Causal Inference in Behavioral Obesity Research, Limited travel scholarships are available to young investigators. Please apply by Fri 4/1/2016 and be notified of acceptance by Fri 4/8/2016. Women, members of underrepresented minority groups and individuals with disabilities are strongly encouraged to apply. We look forward to seeing you in Birmingham this summer!

February 1, 2016

Workshop on Statistical Causal Inference and its Applications to Genetics

Filed under: Announcement — bryantc @ 11:50 pm

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

Statistical Causal Inference and its Applications to Genetics, to be held at CRM in Montreal, July 2529 2016.

Additional information can be found here:

Dear Colleagues,

We are very excited to announce a week long workshop in Statistical Causal Inference and its Applications to Genetics, to be held at CRM in Montreal, July 2529 2016.

We seek participants from Statistics and Biology to discuss the cutting-edge inferential causal problems in the discipline. Points for discussion will include

– modern datasets in genetics,
– methods to deal with huge quantities of data from multiple experimental settings,
– hypothesis generation from limited experimental data,
– efficient experimental design,
– incorporation of prior information in a computationally tractable way,
– causal methods for time series data,
– Mendelian randomization,

We strongly encourage the participation of junior researchers, and invite the submission of abstracts for oral and poster presentations. To register your interest in participating or presenting please visit our website.

Invited speakers include:

Elias Bareinboim (Purdue University)
Tom Claassen (Radboud University Nijmegen)
Denver Dash (University of Pittsburgh)
Philip Dawid (University of Cambridge)
Vanessa Didelez (University of Bristol)
Frederick Eberhardt (Caltech)
Michael Eichler (Maastricht University)
Julien Gagneur (LMU, Gene Center)
Celia Greenwood (Lady Davis Institute for Medical Research)
Niels Richard Hansen (University of Copenhagen)
Dominik Janzing (Max-Planck-Institute for Intelligent Systems)
Samantha Kleinberg (Stevens Institute of Technology)
Aurélie Labbe (McGill University)
Steffen Lauritzen (University of Oxford)
Po-Ling Loh (University of Pennsylvania)
Sisi Ma (New York University)
Daniel Marbach (Université de Lausanne)
John Marioni (EMBL-EBI)
Lawrence McCandless (Simon Fraser University)
Joris Mooij (AMLab, University of Amsterdam)
Dana Pe’er (Columbia University )
Jonas Peters (MPI for Intelligent Systems)
Garvesh Raskutti (University of Wisconsin-Madison)
Thomas S. Richardson (University of Washington)
James Robins (Harvard School of Public Health)
Olli Saarela (University of Toronto)
Karen Sachs (Stanford University)
Shohei Shimizu (Osaka University)
Ricardo Silva (UCL)
George Davey Smith (University of Bristol)
Peter Spirtes (Carnegie Mellon University)
Oliver Stegle (EMBL-EBI)
Simon Tavare (University of Cambridge )
Jin Tian (Iowa State University)
Achim Tresch (Max Planck Institute)
Ioannis Tsamardinos (ICS – FORTH)

Best regards,

The organisers:

Robin Evans, University of Oxford
Chris Holmes, University of Oxford
Marloes Maathuis, ETH Zurich
Erica Moodie, McGill
Ilya Shpitser, Johns Hopkins
David Stephens, McGill
Caroline Uhler, MIT

We’re very grateful to the workshop sponsors: CRM, CANSSI and PIMS.

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