Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

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This poster presents results from applying a new dimension reduction technique (UMAP) to a wide variety of data types, ranging from online text to social networks, for the purpose of creating useful, but anonymized, data. As the dimension reduction procedure produces meaningful distances and supports arbitrary distance measures, it can be applied to a variety of problems, and produces data that is useful for both visualization and predictive modeling. Included is a description of the dimension reduction procedure, the results of its application, and a discussion of planned future use.