Dear friends in causality,
Below are a few items you might find to be of some interest and possibly some challenge.
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:
As you can see, I have had a natural indirect effect on the cover design, but zero controlled direct effect.
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:
Videos of additional lectures will be posted in the near future.
Recent entries on our Causality-Blog include:
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
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/.
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.
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
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
As always, your thoughts are welcome and will surely be put into some good cause when conveyed to other blog readers.