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EVENT DETAILS |
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<P><SPAN>We </SPAN><SPAN>created new types of causal models at the crossroad of Structural Equation Models & Deep Learning. Such models inherit the effective learning & generalization capabilities of modern machine learning tools & the explanatory power of causal models. Based on observational data only (as opposed to experimental/interventional data), we can create data generative models for (eventually large) sets of variables, whose structure is revealing of plausible mechanisms. Our framework, called Causal Generative Neural Network (CGNN) takes as input a un-oriented graph draft (skeleton) & tests various graph structures by minimizing the reproduction error of the joint distribution.</SPAN></P>
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