Causal Analysis in Theory and Practice

Dear colleague in causality research,

This is an End-of-Winter Greetings from the UCLA Causality blog, welcoming you back to a spring-time discussion in causality-related issues.

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

1. Discussions inviting comments

1.1. “Principal Stratification – A goal or a tool?”
http://ftp.cs.ucla.edu/pub/stat_ser/r382.pdf
Posted for discussion by the International Journal of Biostatistics (IJB), this paper questions whether studies based on Principal Stratification target quantities that researchers truly care about.

If you have comments, ideas or objections, you are invited to communicate them to the IJB’s  Editor, “Nicholas P. Jewell” <mm-11332-3261687@bepress.com> or/and, if you wish, cross-post them on this blog.

1.2. “Comments and Controversies: Graphical models, potential outcomes and causal inference: Comment on Lindquist and Sobel”
http://ftp.cs.ucla.edu/pub/stat_ser/r380.pdf

This note comments on a paper published in NeuroImage which argues (yes, again) that the potential outcome model is somehow superior, more rigorous or more principled than the structural models used in fMRI research. To further illuminate the logic of such claims I have added a section (4.4.2) in this paper:
http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf
which demonstrates how potential outcomes can be generated, on demand, from a simple structural model, and no one can tell where they came from. Enjoy.

1.3. “The Causal Mediation Formula – A practitioner guide to the assessment of causal pathways”
http://ftp.cs.ucla.edu/pub/stat_ser/r379.pdf

This paper present mediation analysis to researchers in the tradition of Baron and Keney (1986), and shows through examples how “the percentage explained by mediation” and “the percentage owed to mediation” are estimated in nonlinear models with both continuous and categorical variables.

1.4. Simpson’s Paradox
Sander Greenland brought to my attention a recent paper in Synthese (Sept. 28, 2010) claiming that Simpson’s paradox is NOT rooted in causal, but in some other kind of illusion. I remain convinced of the former, and have accordingly modified the Simpson Paradox entry in Wikipedia to reinforce the causal illusion theory. You might wish to add your take on the subject.

1.5. “The ETT Paradox  (or, the curse of free will)”
This paradox would be appreciated by those who are fascinated, like me, by our ability to determine, from data alone, if one would have been better off acting differently than one actually did. This can lead to a cycle of inevitable regret, and provokes some naughty thoughts.
http://ftp.cs.ucla.edu/pub/stat_ser/r375.pdf

2. New Results

2.1. A newly posted paper, “Controlling Selection Bias in Causal Inference” http://ftp.cs.ucla.edu/pub/stat_ser/r381.pdf gives graphical and algebraic conditions for the removal of selection bias and the recovery of covariate-specific effect measures.

2.2. A new section (Section 5) in http://ftp.cs.ucla.edu/pub/stat_ser/r372.pdf generalizes the concept of transportability from experimental to observational studies,and shows how one can avoid re-learning things from scratch when moving to a new population, new domain, or a new environment.

2.3. After months of struggling with the literature of “surrogate endpoints” we feel that we now have a fairly satisfactory theory of surrogacy. It is based on the idea that a surrogate should serve not merely as a good predictor of outcomes, but also as ROBUST predictor of effects in the face of changing external conditions.  See section 6 in http://ftp.cs.ucla.edu/pub/stat_ser/r372.pdf

3. Courses, Lectures and Conferences

3.1. Causal Inference Course
Thomas Richardson and Michael Hudgens are once again teaching Causal Inference June 13-15, 2011 in the Summer Institute here at U Washington. They have funds to support tuition waivers and some travel for students and postdocs. The website is http://depts.washington.edu/sismid/

3.2. 2011 Atlantic Causal Conference
The 2011 Atlantic Causal Conference will take place at the University of Michigan School of Public Health in Ann Arbor, Michigan, Thursday May 19th and Friday May 20th. See
http://www.sph.umich.edu/biostat/2011acic/index.html for
Contact: Mike Elliott at mrelliot@umich.edu or Ben Hansen at ben.hansen@umich.edu

3.3. Errata for Causality (2010)
FYI, Cambridge University Press has come up with a new printing of my book Causality, which corrects a few errors in the 2009 edition. Please advise students to insist on a copy saying “reprinted 2010”.
If you have an older copy, you can find the corrections marked in red here:
http://bayes.cs.ucla.edu/BOOK-09/errata_scanned_pages7-28-10.pdf

3.4. Lecture Slides available
Slides of my lecture on “What’s New in Causal Inference” can be viewed on my home page, second line from top.
http://bayes.cs.ucla.edu/jp_home.html
You are welcome to use them in any way you choose. But usage for a good cause is recommended.

Looking forward to your postings, and may clarity prevail.

———Judea Pearl
UCLA

Dear colleague in causality research,

This is a belated End-of-Summer greeting from the UCLA Causality blog, welcoming you back to an open discussion of causality-related issues. Below please find four new postings and three hot topics for discussion.

1.  New postings:

Three new papers and several lecture videos have been posted on our website.

1.1. Pearl and Bareinboim, “Transportability across studies: A formal approach,” October 2010.

http://ftp.cs.ucla.edu/pub/stat_ser/r372.pdf

The paper introduces a formal representation for encoding differences between populations and derives procedures for deciding whether (and how) causal effects in the target environment can be inferred from experimental findings in another.

1.2. J. Pearl, “The Causal Foundations of Structural Equation Modeling,” August 2010.

http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf

The paper summarizes how traditional SEM methods can be enriched by modern advances in causal and counterfactual inference.

Click here to the full post.

As always, we welcome your views on this topic. To continue the discussion, please use the comment link below to add your thoughts. You can also suggest a new topic of discussion using our submission form by clicking here.

Dear friends in causality,

Below are a few items you might find to be of some interest and  possibly some challenge.

1.
A new book containing a collection of recent articles on causation, some tutorial in nature, is now available from College Publications (2010.) Title: Heuristics, Probability and Causality, Editors: R. Dechter, H. Geffner and J. Halpern.

For table of contents, preface and more information please click on:
http://bayes.cs.ucla.edu/TRIBUTE/pearl-tribute2010.htm
As you can see, I have had a natural indirect effect on the cover design, but zero controlled direct effect.

2.
A symposium on causality and related topics by some of the contributors to “Heuristics, Probabilities and Causality” was held at UCLA on March 12. Videos of lectures, by: C. Hitchcock, S. Greenland, T. Richardson, J. Robins, R. Scheines, J. Tian, Y. Shoham and J. Pearl, can be viewed here:
http://bayes.cs.ucla.edu/TRIBUTE/tribute-videos.htm
Videos of additional lectures will be posted in the near future.

3.
Recent entries on our Causality-Blog include:

3.1.
An open letter from Judea Pearl to Nancy Cartwright concerning “Causal Pluralism”, a topic central to a discussion of her book “Hunting Causes” which appeared recently in Economics and Philosophy 26:69-77. (Posted May 31, 2010), and
3.2.
A lively discussion by T. Richardson, J. Robins and J. Pearl on the structure of the causal hierarchy and the scientific roll of untestable counterfactual assumptions. (Posted May 3 and May 15, 2010)

Both are posted on http://causality.cs.ucla.edu/blog/.

4.
A recent posting on my web-page is a paper titled: “The Mediation Formula: A guide to the assessment of causal pathways in non-linear models” which explains why traditional methods of mediation analysis yield distorted results when applied to discrete data, even when correct parametric models are assumed and all parameters are known precisely. The Mediation Formula circumvents these difficulties.
http://ftp.cs.ucla.edu/pub/stat_ser/r363.pdf

5.
Another posting of potential interest is Technical Report R-364, by T. Kyono (Master Thesis), titled: “Commentator: A Front-End User-Interface Module for Graphical and Structural Equation Modeling”. It take a DAG as input and prints (1): all identifiable direct effects, (2) all identifiable causal effects, (3) all (minimal) sets of admissible covariates, (4) all instrumental variables, and (5) (almost) all testable implications of a model. The source code is available upon request. http://ftp.cs.ucla.edu/pub/stat_ser/r364.pdf

6.
Finally, I have received inquiries regarding a slide that I used at NYU, in which an instrumental variable poses as an innocent confounder and, upon adjustment, amplifies, rather than reduces confounding bias. The moral of the story was (and is) that “outcome assignment” is safer to model than “treatment assignment”. The pertinent paper is R-356, or http://ftp.cs.ucla.edu/pub/stat_ser/r356.pdf

7.
As always, your thoughts are welcome and will surely be put into some good cause when conveyed to other blog readers.

Best,
=======Judea Pearl
UCLA

M. Elizabeth Halloran from University of Washington writes:

A 2.5 day course on Causal Inference, with particular applications in infectious diseases,  including causal inference with interference,  is offered June 14-16, 2010, in Seattle at the University of Washington. The course is taught by Thomas Richardson and Michael Hudgens. It is Module 2 of the Summer Institute in Statistics and Modeling in Infectious Diseases.

We have funds to support students and postdocs for tuition waivers and travel. Please apply if possible by April 15, though we would likely consider applications after that.

More information and registration is available at http://depts.washington.edu/sismid

Dear blogger,

A few causality-related papers have recently been posted on my website:

• R-357: J. Pearl, “On Measurement Bias in Causal Inference,” http://ftp.cs.ucla.edu/pub/stat_ser/r357.pdf
• R-358: J. Pearl, “On the Consistency Rule in Causal Inference: An Axiom, Definition, Assumption, or a Theorem?,” http://ftp.cs.ucla.edu/pub/stat_ser/r358.pdf
• R-359: J. Pearl, “Physical and Metaphysical Counterfactuals,” http://ftp.cs.ucla.edu/pub/stat_ser/r359.pdf
• R-360: J. Pearl, “If Oswald Hadn’t Used Counterfactuals, My Robot Would Have,” http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf
• R-361: S. Greenland and J. Pearl, “Causal Diagrams,” http://ftp.cs.ucla.edu/pub/stat_ser/r361.pdf

As usual, comments, reservations and objections are most welcome, and can be posted on this forum.

And may that every brilliant thought be put to some good cause.

Dear friends in causality,

This is a year-end greeting from the UCLA causality blog, bringing you the latest news, conjectures, paradoxes, controversies, and occasional progress in causality research.

I have four items for your attention:

1. An Errata page for Chapter 11 of Causality (2009) 2nd Edition has been posted on: http://bayes.cs.ucla.edu/BOOK-09/causality2-errata09.pdf. An electronic version of the book will hit the air as soon as the Epilogue artwork receives copyright permissions.
2. Our causality-blog has been enriched with two recent discussions: ” The intuition behind ‘Inverse Probability Weighting’” and “Accounting for measurement cost and estimators variance“.
3. Several new articles are now posted on my website http://bayes.cs.ucla.edu/csl_papers.html.Below are brief summaries:R-356 deals with “A class of bias-amplifying covariates” that pose as innocent confounders but react nastily when adjusted for. http://ftp.cs.ucla.edu/pub/stat_ser/r356.pdf
   
   R-351, R-354, and R-355 are three variants of my tutorial article in Statistics Survey (R-350), enriched with special slants for their respective audiences.

   R-351 – “The Structural Theory of Causation” is geared to philosophers and has a section contrasting “Structural vs. Probabilistic Causality” (Section 1.5) http://ftp.cs.ucla.edu/pub/stat_ser/r351.pdf

   R-354 – “An Introduction to Causal Inference,” is written for I.J.Biostatistics, and discusses (with examples) how the Mediation Formula can be used to estimate direct and indirect effects from categorical data. http://ftp.cs.ucla.edu/pub/stat_ser/r354.pdf

   R-355 “Causality in the Social and Behavioral Sciences” is written for Sociological Methodology and adds a section on the transition from identification to estimation and testing. http://ftp.cs.ucla.edu/pub/stat_ser/r355.pdf

4. In response to growing concerns among readers about the proliferation of ambiguous terms in the causality literature, we would like to open a new forum: Wiki-Glossary of Causal Terminology. The idea is, as the name implies, to provide a democratic, loosely moderated arena for users to question, propose, shape and refine technical terms that appear ambiguous, puzzling or misused.If you ever encountered such terms and were afraid to ask, this would be a way to share your predicaments with others (anonymously, if you prefer). I have a bunch of such questions myself (e.g., anyone knows what “heterogeneity” is? or “is there a difference between “stratify on”, “control for” “adjust for”, “condition on”, etc.) but I would rather post your questions first and, hopefully, a common-sensical consensus will emerge, restoring clarity to causality.Please submit questions and ideas about the Glossary by leaving a comment, and we will post/e-mail another announcement when the Wiki-Glossary site is up and running.

Wishing you a happy holiday season,
And may every bright thought be put to some good cause!

Judea Pearl writes:

Judea Pearl writes:

The following material is given to people who will attend my tutorial at the JSA meeting August 5 2009, But it might also be of interest to other students of causality. The survey article: "Causal inference in statistics: An Overview" is a recent submission to Statistics Survey which condences everything I know about causality in only 40 pages.

The material may be accessed here: http://www.cs.ucla.edu/~judea/jsm09.

Tutorial
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.

Several of Professor Pearl's recent seminars on causality are now available for streaming online. Please follow the links below to view them.

• State of Causality in Economics (A Joint Seminar with UCLA, UCI, UCSD) (Requires RealPlayer)
• A Seminar given to Johns Hopkins University Bloomberg School of Public Health
• A Seminar given to USC Information Sciences Institute

Please feel free to post any comments or questions about these videos in the comments section.