Dear colleagues in causality research, I am pleased to bring to your attention the following two discussions that were posted on our blog since our last greeting, concerning the foundations of Data-Science.
At its core, I argue that radical empiricism is a stifling culture. It lures researchers into a data-centric paradigm, according to which Data is the source of all knowledge rather than a window through which we learn about the world around us. Instead, I propose a hybrid framework that supplements data with domain knowledge, commonsense constraints, culturally transmitted concepts, and most importantly, our innate causal templates that enable toddlers to quickly acquire an understanding of their toy-world environment. The posts are linked here: (1) Data versus Science: Contesting the Soul of Data-Science (July 7, 2020) https://ucla.in/3iEDRVo (2) Radical Empiricism and Machine Learning Research (July 26, 2020) https://ucla.in/32YKcWy Lively discussions of these and other topics are also taking place on Tweeter, @yudapearl, where I am hoping to convince statisticians, economists, and machine-learning folks of the inevitability of overhauling Data-Science ground up. You are welcome to join the conversation. Finally, the following reports have been added or updated to our blog: "Causally Colored Reflections on Leo Breiman's "Statistical Modeling: The Two Cultures" https://ucla.in/33VqniK Race, COVID Mortality, and Simpson's Paradox (by Dana Mackenzie, July 6, 2020) https://ucla.in/3gy17m8 "A Crash Course in Good and Bad Controls," (by Cinelli, Forney and Pearl, updated Aug.2020) https://ucla.in/2UEwpPd "Graphical Models for Processing Missing Data" (by Mohan and Pearl, updated October 26, 2020) https://ucla.in/2LdEjZW Wishing you all a triumphant exit to a COVID-free world and a productive storm of new research. Judea Pearl UCLA Computer Science Department ju...@cs.ucla.edu, @yudapearl HOME: http://bayes.cs.ucla.edu/jp_home.html "Book of Why" http://bayes.cs.ucla.edu/WHY/ _______________________________________________ uai mailing list uai@engr.orst.edu https://it.engineering.oregonstate.edu/mailman/listinfo/uai
