Published online by Cambridge University Press: 01 January 2022
Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—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.
This research is undertaken under the auspices of the University of Pittsburgh Carnegie Mellon Center for Causal Discovery, supported by the National Institutes of Health under award U54HG008540. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional support was received from the James S. McDonnell Foundation. We thank Gregory Cooper, Xinghua Lu, and Richard Scheines for their help.