Fall Greetings from UCLA Causality Blog
Friends in causality research,
This Fall greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, new problems and new solutions.
A. News items concerning causality research
A1. The American Statistical Association has announced an early submission deadline for the 2015 “Causality in Statistics Education Award” — February 15, 2015.
For details and selection criteria, see http://www.amstat.org/education/causalityprize/
A2. Vol. 2 Issue 2 of the Journal of Causal Inference (JCI) is now out, and can be viewed here:
http://www.degruyter.com/view/j/jci.2014.2.issue-2/issue-files/jci.2014.2.issue-2.xml
As always, submissions are welcome on all aspects of causal analysis, especially those deemed methodological.
A3. New Tutorial: Causality for Policy Assessment and Impact Analysis, is offered by BayesiaLab , see here.
A4. A Conference on Counterfactual anaysis for Policy Evaluation will take place at USC, November 20, 2014
http://dornsife.usc.edu/conferences/cafe-conference-2014/
A5. A Conference focused on Causal Inference will take place at Kyoto, Japan, November 17-18, 2014
Kyoto International Conference on Modern Statistics in the 21st Century
General info: http://www.kakenhyoka.jp/conference/index_en.html
Program: http://www.kakenhyoka.jp/conference/file/program.pdf
B. New postings, new problems and new solutions.
B1. A confession of a graph-avoiding econometrician.
Guido Imbens explains why some economists do not find causal graphs to be helpful. Miquel Porta describes the impact of causal graphs in epidemiology as a “revolution”. The question naturally arises: “Are economists smarter than epidemiologists?” or, “What drives epidemiologists to seek the light of new tools while graph-avoiding economists resign to parial blindness?”
See [link] for attempted answer.
B2. Lord’s Paradox Revisited — (Oh Lord! Kumbaya!)
This is a historical journey which traces back Lord’s paradox from its original formulation (1967), resolves it using modern tools of causal analysis, explains why it presented difficulties in previous attempts at resolution and, finally, addresses the general issue of whether adjustments for pre-existing conditions is justified in group comparison applications.
Link: http://ftp.cs.ucla.edu/pub/stat_ser/r436.pdf
B3. “Causes of Effects and Effects of Causes”
http://ftp.cs.ucla.edu/pub/stat_ser/r431.pdf
An expansion of a previous note with same title, including additional demonstration that “causes of effects” are not metaphysical (Dawid, 2000) and a simple visualization of how the probability of necessity (PN) is shaped by experimental and observational findings. It comes together with “A note on Causes of Effects” link a rebuttal to recent attempts at mystification.