This talk is part 12 of the Workshop on Case Studies of Causal Discovery with Model Search, held on October 25-27, 2013, at Carnegie Mellon University. Speaker: Bill Shipley Title: The worldwide leaf economic spectrum: How causal discovery algorithms forced me to re-imagine its generating causes Abstract: This story begins with four leaf traits related to the main "economic" function of a leaf (i.e. to fix carbon that contributes to growth, reproduction and survival): specific leaf mass (SLM, the ratio of leaf dry mass: leaf projected surface area), leaf nitrogen content per mass (Nm, the mass of nitrogen per unit leaf dry mass), maximum leaf net photosynthetic rate (Am, the net amount of carbon fixed per unit leaf mass per unit time) and leaf lifespan (L, the average number of days before programmed leaf death). A large body of evidence shows that species of vascular plants all over the world, typical of every type of habit, display the same pattern of correlations between these variables. What causes this common pattern of covariation? It is impossible to answer question this using experimental manipulations. I (and many others) thought that we understood this from basic physiology but structural equations modelling shows us to be wrong. I explain how the Fast Causal Inference algorithm and the Tetrad representation theorem led me to a new hypothesis. Link to slides: TBA Link to workshop homepage: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php Workshop description: Computer scientists, statisticians, and philosophers have created a precise mathematical framework for representing causal systems called "Graphical Causal Models." This framework has supported the rigorous description of causal model spaces and the notion of empirical indistinguishability/equivalence within such spaces, which has in turn enabled computer scientists to develop asymptotically reliable model search algorithms for efficiently searching these spaces. The conditions under which these methods are practically useful in applied science was the topic of this workshop. The workshop brought together scholars from genetics, biology, economics, fMRI-based cognitive neuroscience, climate research, education research, and several other disciplines, all of whom have successfully applied computerized search for causal models toward a scientifically challenging problem. The goals for the workshop were: (1) to identify strategies for applying causal model search to diverse domain-specific scientific questions; (2) to identify and discuss methodological challenges that arise when applying causal model search to real-world scientific problems; and (3) to take concrete steps toward creating an interdisciplinary community of researchers interested in applied causal model search. We welcomed junior scholars and graduate students, and we hosted a free introductory tutorial on model search the first morning of the workshop. Funding for this workshop was provided by the National Science Foundation, grant #SES1156001, and by the Center for Formal Epistemology at Carnegie Mellon University. Thanks to their support, we were able both to host the workshop, and also record it, making it accessible to a larger audience.

• published: 16 Dec 2013
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