Friends in causality research,<\/p>\n
This Spring greeting from UCLA Causality blog contains:
\nA. News items concerning causality research,
\nB. New postings, new problems and new solutions.<\/p>\n
A. News items concerning causality research<\/strong> Another good news, Google has joined Microsoft in sponsoring next year’s award, so please upgrade your 2016 nominations. For details of nominations and selection criteria, see http:\/\/www.amstat.org\/education\/causalityprize\/<\/a><\/p>\n A2.<\/strong> Vol. 3 Issue 1 (March 2015) of the Journal of Causal Inference (JCI) is now in print. A3.<\/strong> 2015 Atlantic Causal Inference A4.<\/strong> A 2-Day Course: Causal Inference with Graphical Models will be offered in San Jose, CA, on June 15-16, by professor Felix Elwert (University of Wisconsin). The organizers (BayesiaLab) offer generous dacademic discounts to students and faculty. See here<\/a>.<\/p>\n B. New postings, new problems and new solutions.<\/strong><\/p>\n B1.<\/strong> Causality and Big data<\/p>\n The National Academy of Sciences has organized a colloquium on “Drawing Causal Inference from Big Data”. The colloquium took place March 26-27, in Washington DC, and reflected a growing realization that statistical analysis void of causal explanations would not satisfy users of big data systems. The colloquium program can be viewed here: My talk (with E. Bareinboim) focused on the problem of fusing data from multiple sources so as to provide valid answers to causal questions of interest. The main point was that this seemingly hopeless task can now be reduced to mathematics. See abstract and slides here: http:\/\/www.nasonline.org\/programs\/sackler-colloquia\/documents\/pearl1.pdf<\/a> B2.<\/strong> A recent post on our blog <\/a> deals with one of the most crucial and puzzling questions of causal inference: “How generalizable are our randomized clinical trials?” It turns out that the tools developed for transportability theory in http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r400.pdf<\/a> also provide an elegant answer to this question. Our post compares this answer to the way researchers have attempted to tackle the problem using the language of ignorability, usually resorting to post-stratification. It turns out that ignorability-type assumptions are fairly limited, both in their ability to define conditions that permit generalizations, and in the way they impede interpretation in specific applications.<\/p>\n B3.<\/strong> We welcome the journal publication of the following research reports, Please update your citations:<\/p>\n B3.1<\/strong> On the interpretation and Identification of mediation B3.2<\/strong> On transportability B3.3<\/strong> Back to mediation
\nA1.<\/strong> Congratulations go to Tyler VanderWeele, winner of the 2015 ASA “Causality in Statistics Education Award” for his book “Explanation in Causal Inference” (Oxford, 2015). Thanks, Tyler. The award ceremony will take place at the 2015 JSM conference, August 8-13, in Seattle.<\/p>\n
\nThe Table of Content and full text pdf can be viewed here<\/a>. Submissions are welcome on all aspects of causal analysis. A highly urgent request is in place: Please start your article with a crisp description of the research problem addressed.<\/p>\n
\nThe 2015 Atlantic Causal Conference will take place in Philadelphia, May 20th through May 21 2015. The web site for the registration and conference is http:\/\/www.med.upenn.edu\/cceb\/biostat\/conferences\/ACIC15\/index_acic15.php<\/a><\/p>\n
\nhttp:\/\/www.nasonline.org\/programs\/sackler-colloquia\/completed_colloquia\/Big-data.html<\/a><\/p>\n
\nand a youtube video here: https:\/\/www.youtube.com\/watch?v=sjtBalq7Ulc<\/a><\/p>\n
\nLink: http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r389.pdf<\/a><\/p>\n
\nLink: http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r400.pdf<\/a><\/p>\n
\nLink: http:\/\/ftp.cs.ucla.edu\/pub\/stat_ser\/r421-reprint.pdf<\/a><\/p>\n