Dear friends in causality research,
This greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, publications, slides and videos,
C. New scientific questions and some answers.
A. News items concerning causality research
A.1 The American Statistical Association has announced the 2014 winners of the “Causality in Statistics Education Award.” See http://www.amstat.org/newsroom/pressreleases/2014-CausalityinStatEdAward.pdf
Congratulations go to the honorees, Maya Peterson and Laura B. Balzer (UC Berkeley, biostatistics department), who will each receive a $5000 and a plaque at the 2014 Joint Statistical Meetings (JSM 2014) in Boston.
A.2 Vol. 2 Issue 2 of the Journal of Causal Inference (JCI) is scheduled to appear September, 2014. The TOC can be viewed here: http://degruyter.com/view/j/jci (click on READ CONTENT, under the cover picture)
As always, submissions are welcome on all aspects of causal analysis, especially those deemed heretical.
A.3 The 2014 World Congress on Epidemiology (IEA) will include a pre-conference program with two short courses dedicated to causal inference.
IEA-2014, Anchorage , Alaska, August 16, 2014,
B. New postings, publications, slides and videos
B1. An interesting blog page dedicated to Sewall Wright’s 1921 paper “Correlation and causation” can be viewed here http://evaluatehelp.blogspot.com/2014/05/wright1st.html
It is intruiging to see how the first causal diagram came to the attention of the scientific community, in 1921. (It was immediately attacked, of course, by students of Karl Pearson.)
B.2 A video of my recent interview with professor Nick Jewell (UC Berkeley) concerning Causal Inference in Statistics, can now be watched by going to www.statisticsviews.com and clicking on the link next to the image.
B.3 A new review of Causality (Cambridge, 2009) has appeared in the Journal of Structural Equation Models, authored by Stephen West and Tobias Koch. See http://bayes.cs.ucla.edu/BOOK-2K/west-koch-review2014.pdf
My comments on this review will be posted here in a few days; stay tuned.
B.4 The paper “Trygve Haavelmo and the Emergence of Causal Calculus” is now available online on Econometric Theory, (10 June 2014), see here.
To the best of my knowledge, this is the first article on modern causal analysis that managed to penetrate the walls of mainstream econometric literature. Only time will tell whether this publication would help soften the enigmatic resistance of traditional economists to modern tools of causal analysis. Oddly, even those economists who have came to accept the structural reading of counterfactuals (e.g., Heckman and Pinto, 2013) still find it difficult to accept the second principle of causal inference: reading independencies from the model’s structure. See http://ftp.cs.ucla.edu/pub/stat_ser/r420.pdf
At any rate, the editors, Olav Bjerkholt and Peter Phillips, deserve a medal of courage for their heroic effort to create a dialogue between two civilizations.
B.5 To further facilitate this dialogue, Bryant Chen and I wrote a survey paper http://ftp.cs.ucla.edu/pub/stat_ser/r428.pdf which summarizes and illustrates the benefits of graphical tools in the context of linear models, where most economists feel secure and comfortable.
C. New scientific questions and some answers
There are new postings on our home page http://bayes.cs.ucla.edu/csl_papers.html that might earn your attention. Among them:
R-425 “Recovering from Selection Bias in Causal and Statistical Inference,” with E. Bareinboim and J. Tian,
We ask: Is there a general, non-parametric solution to the selection-bias problem posed by Berkson
and Heckman decades ago?
The answer is: Yes. The problem is illuminated, generalized and solved using graphical models — the language where knowledge resides.
(The article just received the Best Paper Award at the Annual Conference of the American Association for Artificial Intelligence (AAAI-2014), July 30, 2014.)
R-431. “Causes of effects and Effects of Causes”.
Question: Is it really the case that modern methods of causal analysis have neglected to deal with “causes of effects”, as claimed by a recent paper of Dawid, Fienberg and Faigman (2013)?.
Answer: Quite the contrary! See here:
R-428. “Testable Implications of Linear Structural Equation Models” with Bryant Chen and Jin Tian.
We ask: Is there a systematic way of unveiling the testable implications of a linear model with latent variables?
Answer: We provide an algorithm for doing so.
Finally, dont miss previous postings on our blog, for example:
1. On Simpson’s Paradox. Again?
2. Who Needs Causal Mediation?
3. On model-based vs. ad-hoc methods
Wishing you a productive summer,